Medical Image Benchmarks

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Overview

Medical image benchmarks provide datasets, annotations, and shared protocols for comparing systems on tasks such as segmentation, diagnosis, image synthesis, and report generation across modalities including CT, MRI, X-rays, ultrasound, and pathology. They evaluate methods ranging from U-Net and nnU-Net models to vision transformers, foundation models, and multimodal language models, using measures such as Dice, AUROC, calibration, and expert review. A central challenge is whether strong results transfer across patients, hospitals, scanners, populations, and difficult cases; external testing, patient-level splits, and audits for leakage and shortcuts help expose gaps hidden by average scores. The field is moving toward workflow-based and evidence-grounded evaluation, but there is still tension between standardized, easily comparable scores and measures that reflect clinical safety, uncertainty, and real-world usefulness.

Latest papers

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339 papers31 papers in the last four weeks, up 7% on the four weeks before. 0.2% of all new papers.

Apr 12, 2026

WBCBench 2026: A Challenge for Robust White Blood Cell Classification Under Class Imbalance

We present WBCBench 2026, an ISBI challenge and benchmark for automated WBC classification designed to stress-test algorithms under three key difficulties: (i) severe class imbalance across 13 morphologically fine-grained WBC classes, (ii) strict patient-level separation between training, validation and test sets, and (iii) synthetic scanner- and setting-induced domain shift via controlled noise, blur and illumination perturbations. All images are single-site microscopic blood smear acquisitions with standardised staining and expert hematopathologist annotations. This paper reviews the challenge and summarises the proposed solutions and final outcomes. The benchmark is organised into two phases. Phase 1 provides a pristine training set. Phase 2 introduces degraded images with split-specific severity distributions for train, validation and test, emulating a realistic shift between development and deployment conditions. We specify a standardised submission schema, open-source evaluator, and macro-averaged F1 score as the primary ranking metric.

Xin Tian, Xudong Ma, Tianqi Yang +4

University of Oxford, Oxford, United Kingdom · University College London, London, United Kingdom · University of Bristol, Bristol, United Kingdom +1

White Blood Cell ClassificationMedical Image ClassificationMedical Image BenchmarksDistribution Shift RobustnessFine-Grained Image ClassificationClass-Imbalanced Learning
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Apr 12, 2026

MMRareBench: A Rare-Disease Multimodal and Multi-Image Medical Benchmark

Multimodal large language models (MLLMs) have advanced clinical tasks for common conditions, but their performance on rare diseases remains largely untested. In rare-disease scenarios, clinicians often lack prior clinical knowledge, forcing them to rely strictly on case-level evidence for clinical judgments. Existing benchmarks predominantly evaluate common-condition, single-image settings, leaving multimodal and multi-image evidence integration under rare-disease data scarcity systematically unevaluated. We introduce MMRareBench, to our knowledge the first rare-disease benchmark jointly evaluating multimodal and multi-image clinical capability across four workflow-aligned tracks: diagnosis, treatment planning, cross-image evidence alignment, and examination suggestion. The benchmark comprises 1,756 question-answer pairs with 7,958 associated medical images curated from PMC case reports, with Orphanet-anchored ontology alignment, track-specific leakage control, evidence-grounded annotations, and a two-level evaluation protocol. A systematic evaluation of 23 MLLMs reveals fragmented capability profiles and universally low treatment-planning performance, with medical-domain models trailing general-purpose MLLMs substantially on multi-image tracks despite competitive diagnostic scores. These patterns are consistent with a capacity dilution effect: medical fine-tuning can narrow the diagnostic gap but may erode the compositional multi-image capability that rare-disease evidence integration demands.

Junzhi Ning, Jiashi Lin, Yingying Fang +9

Shanghai AI Laboratory · Imperial College London · Shanghai Jiao Tong University +1

Medical Image BenchmarksMultimodal Model EvaluationMultimodal Large Language ModelsRare Disease Diagnosis
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Apr 10, 2026

Benchmarking CNN- and Transformer-Based Models for Surgical Instrument Segmentation in Robotic-Assisted Surgery

Accurate segmentation of surgical instruments in robotic-assisted surgery is critical for enabling context-aware computer-assisted interventions, such as tool tracking, workflow analysis, and autonomous decision-making. In this study, we benchmark five deep learning architectures-UNet, UNet, DeepLabV3, Attention UNet, and SegFormer on the SAR-RARP50 dataset for multi-class semantic segmentation of surgical instruments in real-world radical prostatectomy videos. The models are trained with a compound loss function combining Cross Entropy and Dice loss to address class imbalance and capture fine object boundaries. Our experiments reveal that while convolutional models such as UNet and Attention UNet provide strong baseline performance, DeepLabV3 achieves results comparable to SegFormer, demonstrating the effectiveness of atrous convolution and multi-scale context aggregation in capturing complex surgical scenes. Transformer-based architectures like SegFormer further enhance global contextual understanding, leading to improved generalization across varying instrument appearances and surgical conditions. This work provides a comprehensive comparison and practical insights for selecting segmentation models in surgical AI applications, highlighting the trade-offs between convolutional and transformer-based approaches.

Sara Ameli

Medical Image SegmentationSemantic SegmentationSurgical RoboticsConvolutional Neural NetworksVision TransformerMedical Image Benchmarks
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Apr 9, 2026

Benchmarking Deep Learning for Future Liver Remnant Segmentation in Colorectal Liver Metastasis

Accurate segmentation of the future liver remnant (FLR) is critical for surgical planning in colorectal liver metastases (CRLM) to prevent fatal post-hepatectomy liver failure. However, this segmentation task is technically challenging due to complex resection boundaries, convoluted hepatic vasculature and diffuse metastatic lesions. A primary bottleneck in developing automated AI tools has been the lack of high-fidelity, validated data. We address this gap by manually refining all 197 volumes from the public CRLM-CT-Seg dataset, creating the first open-source, validated benchmark for this task. We then establish the first segmentation baselines, comparing cascaded (Liver->CRLM->FLR) and end-to-end (E2E) strategies using nnU-Net, SwinUNETR, and STU-Net. We find a cascaded nnU-Net achieves the best final FLR segmentation Dice (0.767), while the pretrained STU-Net provides superior CRLM segmentation (0.620 Dice) and is significantly more robust to cascaded errors. This work provides the first validated benchmark and a reproducible framework to accelerate research in AI-assisted surgical planning.

Anthony T. Wu, Arghavan Rezvani, Kela Liu +4

University of California, Irvine Donald Bren School of Information and Computer Science · University of California, Irvine School of Medicine

Medical Image Segmentation3D Medical Image SegmentationMedical Image Benchmarks
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Apr 7, 2026

Extending ZACH-ViT to Robust Medical Imaging: Corruption and Adversarial Stress Testing in Low-Data Regimes

The recently introduced ZACH-ViT (Zero-token Adaptive Compact Hierarchical Vision Transformer) formalized a compact permutation-invariant Vision Transformer for medical imaging and argued that architectural alignment with spatial structure can matter more than universal benchmark dominance. Its design was motivated by the observation that positional embeddings and a dedicated class token encode fixed spatial assumptions that may be suboptimal when spatial organization is weakly informative, locally distributed, or variable across biomedical images. The foundational study established a regime-dependent clean performance profile across MedMNIST, but did not examine robustness in detail. In this work, we present the first robustness-focused extension of ZACH-ViT by evaluating its behavior under common image corruptions and adversarial perturbations in the same low-data setting. We compare ZACH-ViT with three scratch-trained compact baselines, ABMIL, Minimal-ViT, and TransMIL, on seven MedMNIST datasets using 50 samples per class, fixed hyperparameters, and five random seeds. Across the benchmark, ZACH-ViT achieves the best overall mean rank on clean data (1.57) and under common corruptions (1.57), indicating a favorable balance between baseline predictive performance and robustness to realistic image degradation. Under adversarial stress, all models deteriorate substantially; nevertheless, ZACH-ViT remains competitive, ranking first under FGSM (2.00) and second under PGD (2.29), where ABMIL performs best overall. These results extend the original ZACH-ViT narrative: the advantages of compact permutation-invariant transformers are not limited to clean evaluation, but can persist under realistic perturbation stress in low-data medical imaging, while adversarial robustness remains an open challenge for all evaluated models.

Athanasios Angelakis, Marta Gomez-Barrero

BioML Lab, RI CODE, UniBw, Munich, Germany · AUMC, Amsterdam, Netherlands

Vision TransformerMedical Image ClassificationImage Corruption RobustnessAdversarial RobustnessMedical Image Benchmarks
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Apr 7, 2026

MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models

Medical Vision-Language Models (Med-VLMs) have achieved expert-level proficiency in interpreting diagnostic imaging. However, current models are predominantly trained on professional literature, limiting their ability to communicate findings in the lay register required for patient-centered care. While text-centric research has actively developed resources for simplifying medical jargon, there is a critical absence of large-scale multimodal benchmarks designed to facilitate lay-accessible medical image understanding. To bridge this resource gap, we introduce MedLayBench-V, the first large-scale multimodal benchmark dedicated to expert-lay semantic alignment. Unlike naive simplification approaches that risk hallucination, our dataset is constructed via a Structured Concept-Grounded Refinement (SCGR) pipeline. This method enforces strict semantic equivalence by integrating Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs) with micro-level entity constraints. MedLayBench-V provides a verified foundation for training and evaluating next-generation Med-VLMs capable of bridging the communication divide between clinical experts and patients.

Han Jang, Junhyeok Lee, Heeseong Eum +1

Seoul National University · Department of Radiology, Seoul National University Hospital · The Advanced Imaging and Computational Neuroimaging (AICON) Laboratory +2

HealthcareMedical VLMsVision-Language AlignmentClinical NLPMedical Image Benchmarks
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Apr 6, 2026

Temporal Inversion for Learning Interval Change in Chest X-Rays

Recent advances in vision--language pretraining have enabled strong medical foundation models, yet most analyze radiographs in isolation, overlooking the key clinical task of comparing prior and current images to assess interval change. For chest radiographs (CXRs), capturing interval change is essential, as radiologists must evaluate not only the static appearance of findings but also how they evolve over time. We introduce TILA (Temporal Inversion-aware Learning and Alignment), a simple yet effective framework that uses temporal inversion, reversing image pairs, as a supervisory signal to enhance the sensitivity of existing temporal vision-language models to directional change. TILA integrates inversion-aware objectives across pretraining, fine-tuning, and inference, complementing conventional appearance modeling with explicit learning of temporal order. We also propose a unified evaluation protocol to assess order sensitivity and consistency under temporal inversion, and introduce MS-CXR-Tretrieval, a retrieval evaluation set constructed through a general protocol that can be applied to any temporal CXR dataset. Experiments on public datasets and real-world hospital cohorts demonstrate that TILA consistently improves progression classification and temporal embedding alignment when applied to multiple existing architectures.

Hanbin Ko, Kyungmin Jeon, Doowoong Choi +1

Interdisciplinary Program in Bioengineering, Seoul National University Graduate School · Integrated Major in Innovative Medical Science, Seoul National University Graduate School · Dept. of Radiology, Seoul National University Hospital +2

Longitudinal Medical Image AnalysisMedical VLMsChest X-Ray ClassificationMedical Image Benchmarks
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Apr 4, 2026

Bridging Restoration and Diagnosis: A Comprehensive Benchmark for Retinal Fundus Enhancement

Over the past decade, generative models have demonstrated success in enhancing fundus images. However, the evaluation of these models remains a challenge. A benchmark for fundus image enhancement is needed for three main reasons:(1) Conventional denoising metrics such as PSNR and SSIM fail to capture clinically relevant features, such as lesion preservation and vessel morphology consistency, limiting their applicability in real-world settings; (2) There is a lack of unified evaluation protocols that address both paired and unpaired enhancement methods, particularly those guided by clinical expertise; and (3) An evaluation framework should provide actionable insights to guide future advancements in clinically aligned enhancement models. To address these gaps, we introduce EyeBench-V2, a benchmark designed to bridge the gap between enhancement model performance and clinical utility. Our work offers three key contributions:(1) Multi-dimensional clinical-alignment through downstream evaluations: Beyond standard enhancement metrics, we assess performance across clinically meaningful tasks including vessel segmentation, diabetic retinopathy (DR) grading, generalization to unseen noise patterns, and lesion segmentation. (2) Expert-guided evaluation design: We curate a novel dataset enabling fair comparisons between paired and unpaired enhancement methods, accompanied by a structured manual assessment protocol by medical experts, which evaluates clinically critical aspects such as lesion structure alterations, background color shifts, and the introduction of artificial structures. (3) Actionable insights: Our benchmark provides a rigorous, task-oriented analysis of existing generative models, equipping clinical researchers with the evidence needed to make informed decisions, while also identifying limitations in current methods to inform the design of next-generation enhancement models.

Xuanzhao Dong, Wenhui Zhu, Xiwen Chen +8

Arizona State University · Clemson University · LinkedIn Corporation +1

Medical Image Quality AssessmentImage EnhancementMedical Image BenchmarksMedical Image Analysis
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Apr 2, 2026

Managing Diabetic Retinopathy with Deep Learning: A Data Centric Overview

Diabetic Retinopathy (DR) is a serious microvascular complication of diabetes, and one of the leading causes of vision loss worldwide. Although automated detection and grading, with Deep Learning (DL), can reduce the burden on ophthalmologists, it is constrained by the limited availability of high-quality datasets. Existing repositories often remain geographically narrow, contain limited samples, and exhibit inconsistent annotations or variable image quality; thereby, restricting their clinical reliability. This paper presents a comprehensive review and comparative analysis of fundus image datasets used in the management of DR. The study evaluates their usability across key tasks, including binary classification, severity grading, lesion localization, and multi-disease screening. It also categorizes the datasets by size, accessibility, and annotation type (such as image-level, lesion-level, and multi-disease). Finally, a recently published dataset is presented as a case study to illustrate broader challenges in dataset curation and usage. The review consolidates current knowledge while highlighting persistent gaps such as the lack of standardized lesion-level annotations and longitudinal data. It also outlines recommendations for future dataset development to support clinically reliable and explainable solutions in DR screening.

Shramana Dey, Zahir Khan, T. A. PramodKumar +5

Indian Statistical Institute, Kolkata, West Bengal, India · Dr. Mohan’s Diabetes Specialties Centre, Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India · Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal, India +1

Medical Image BenchmarksDiabetic Retinopathy GradingFundus Imaging
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Apr 2, 2026

Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models

The rapid growth of medical imaging has fueled the development of Foundation Models (FMs) to reduce the growing, unsustainable workload on radiologists. While recent FMs have shown the power of large-scale pre-training to CT and MRI analysis, there remains significant room to optimize how these models learn from complex radiological volumes. Building upon the Curia framework, this work introduces Curia-2, which significantly improves the original pre-training strategy and representation quality to better capture the specificities of radiological data. The proposed methodology enables scaling the architecture up to billion-parameter Vision Transformers, marking a first for multi-modal CT and MRI FMs. Furthermore, we formalize the evaluation of these models by extending and restructuring CuriaBench into two distinct tracks: a 2D track tailored for slice-based vision models and a 3D track for volumetric benchmarking. Our results demonstrate that Curia-2 outperforms all FMs on vision-focused tasks and fairs competitively to vision-language models on clinically complex tasks such as finding detection. Weights will be made publicly available to foster further research.

Antoine Saporta, Baptiste Callard, Corentin Dancette +5

Raidium, 27 rue du Faubourg Saint-Jacques, 75014 Paris, France. · Department of Vascular and Oncological Interventional Radiology, Hôpital Européen Georges Pompidou, AP-HP, Paris, France · Faculté de Santé, Université Paris-Cité, Paris, France.

Medical Imaging Foundation ModelsMedical ImagingMedical Image Representation LearningMedical Image Benchmarks
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Mar 30, 2026

DinoDental: Benchmarking DINOv3 as a Unified Vision Encoder for Dental Image Analysis

The scarcity and high cost of expert annotations in dental imaging present a significant challenge for the development of AI in dentistry. DINOv3, a state-of-the-art, self-supervised vision foundation model pre-trained on 1.7 billion images, offers a promising pathway to mitigate this issue. However, its reliability when transferred to the dental domain, with its unique imaging characteristics and clinical subtleties, remains unclear. To address this, we introduce DinoDental, a unified benchmark designed to systematically evaluate whether DINOv3 can serve as a reliable, off-the-shelf encoder for comprehensive dental image analysis without requiring domain-specific pre-training. Constructed from multiple public datasets, DinoDental covers a wide range of tasks, including classification, detection, and instance segmentation on both panoramic radiographs and intraoral photographs. We further analyze the model's transfer performance by scaling its size and input resolution, and by comparing different adaptation strategies, including frozen features, full fine-tuning, and the parameter-efficient Low-Rank Adaptation (LoRA) method. Our experiments show that DINOv3 can serve as a strong unified encoder for dental image analysis across both panoramic radiographs and intraoral photographs, remaining competitive across tasks while showing particularly clear advantages for intraoral image understanding and boundary-sensitive dense prediction. Collectively, DinoDental provides a systematic framework for comprehensively evaluating DINOv3 in dental analysis, establishing a foundational benchmark to guide efficient and effective model selection and adaptation for the dental AI community.

Kun Tang, Xinquan Yang, Mianjie Zheng +6

Shenzhen University. · Shenzhen University General Hospital. · Chongqing University.

Medical Image AnalysisVision Foundation Model AdaptationDental ImagingMedical Imaging Foundation ModelsMedical Image BenchmarksMedical Image Classification
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Mar 27, 2026

External Benchmarking of Lung Ultrasound Models for Pneumothorax-Related Signs: A Manifest-Based Multi-Source Study

Background and Aims: Reproducible external benchmarks for pneumothorax-related lung ultrasound (LUS) AI are scarce, and binary lung-sliding classification may obscure clinically important signs. We therefore developed a manifest-based external benchmark and used it to test both cross-domain generalization and task validity. Methods: We curated 280 clips from 190 publicly accessible LUS source videos and released a reconstruction manifest containing URLs, timestamps, crop coordinates, labels, and probe shape. Labels were normal lung sliding, absent lung sliding, lung point, and lung pulse. A previously published single-site binary classifier was evaluated on this benchmark; challenge-state analysis examined lung point and lung pulse using the predicted probability of absent sliding, P(absent). Results: The single-site comparator achieved ROC-AUC 0.9625 in-domain but 0.7050 on the heterogeneous external benchmark; restricting external evaluation to linear clips still yielded ROC-AUC 0.7212. In challenge-state analysis, mean P(absent) ranked absent (0.504) > lung point (0.313) > normal (0.186) > lung pulse (0.143). Lung pulse differed from absent clips (p=0.000470) but not from normal clips (p=0.813), indicating that the binary model treated pulse as normal-like despite absent sliding. Lung point differed from both absent (p=0.000468) and normal (p=0.000026), supporting its interpretation as an intermediate ambiguity state rather than a clean binary class. Conclusion: A manifest-based, multi-source benchmark can support reproducible external evaluation without redistributing source videos. Binary lung-sliding classification is an incomplete proxy for pneumothorax reasoning because it obscures blind-spot and ambiguity states such as lung pulse and lung point.

Takehiro Ishikawa

College of Computing, Georgia Institute of Technology, 801 Atlantic Drive NW, Atlanta, GA 30332, USA

Medical Image BenchmarksMedical Image ClassificationUltrasound ImagingMedical Image Anomaly Detection
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Mar 26, 2026

Colon-Bench: An Agentic Workflow for Scalable Dense Lesion Annotation in Full-Procedure Colonoscopy Videos

Early screening via colonoscopy is critical for colon cancer prevention, yet developing robust AI systems for this domain is hindered by the lack of densely annotated, long-sequence video datasets. Existing datasets predominantly focus on single-class polyp detection and lack the rich spatial, temporal, and linguistic annotations required to evaluate modern Multimodal Large Language Models (MLLMs). To address this critical gap, we introduce Colon-Bench, generated via a novel multi-stage agentic workflow. Our pipeline seamlessly integrates temporal proposals, bounding-box tracking, AI-driven visual confirmation, and human-in-the-loop review to scalably annotate full-procedure videos. The resulting verified benchmark is unprecedented in scope, encompassing 528 videos, 14 distinct lesion categories (including polyps, ulcers, and bleeding), over 300,000 bounding boxes, 213,000 segmentation masks, and 133,000 words of clinical descriptions. We utilize Colon-Bench to rigorously evaluate state-of-the-art MLLMs across lesion classification, Open-Vocabulary Video Object Segmentation (OV-VOS), and video Visual Question Answering (VQA). The MLLM results demonstrate surprisingly high localization performance in medical domains compared to SAM-3. Finally, we analyze common VQA errors from MLLMs to introduce a novel "colon-skill" prompting strategy, improving zero-shot MLLM performance by up to 9.7% across most MLLMs. The dataset and the code are available at https://abdullahamdi.com/colon-bench .

Abdullah Hamdi, Changchun Yang, Xin Gao

King Abdullah University of Science and Technology (KAUST)

Video Object SegmentationMedical Image AnalysisMultimodal Model EvaluationMedical Image Benchmarks
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Mar 26, 2026

An Image Dataset of Common Skin Diseases of Bangladesh and Benchmarking Performance with Machine Learning Models

Skin diseases are a major public health concern worldwide, and their detection is often challenging without access to dermatological expertise. In countries like Bangladesh, which is highly populated, the number of qualified skin specialists and diagnostic instruments is insufficient to meet the demand. Due to the lack of proper detection and treatment of skin diseases, that may lead to severe health consequences including death. Common properties of skin diseases are, changing the color, texture, and pattern of skin and in this era of artificial intelligence and machine learning, we are able to detect skin diseases by using image processing and computer vision techniques. In response to this challenge, we develop a publicly available dataset focused on common skin disease detection using machine learning techniques. We focus on five prevalent skin diseases in Bangladesh: Contact Dermatitis, Vitiligo, Eczema, Scabies, and Tinea Ringworm. The dataset consists of 1612 images (of which, 250 are distinct while others are augmented), collected directly from patients at the outpatient department of Faridpur Medical College, Faridpur, Bangladesh. The data comprises of 302, 381, 301, 316, and 312 images of Dermatitis, Eczema, Scabies, Tinea Ringworm, and Vitiligo, respectively. Although the data are collected regionally, the selected diseases are common across many countries especially in South Asia, making the dataset potentially valuable for global applications in machine learning-based dermatology. We also apply several machine learning and deep learning models on the dataset and report classification performance. We expect that this research would garner attention from machine learning and deep learning researchers and practitioners working in the field of automated disease diagnosis.

Sazzad Hossain, Saiful Islam, Muhammad Ibrahim +3

Department of Computer Science and Engineering, Faridpur Engineering College, Faridpur, Bangladesh · Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh · Department of Oncology, Labaid Cancer and Super Speciality Center, Dhaka, Bangladesh +1

Skin Lesion ClassificationMedical Image ClassificationDermatologyMedical Image Benchmarks
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Mar 25, 2026

MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows

Medical imaging benchmarks often evaluate VLMs on pre-selected 2D images, slices, crops, or patches, making evaluation closer to visual recognition. Real clinical workflows impose a different burden: readers must search through complete studies, operate imaging software, navigate across slices and magnifications, and document visual evidence that can be audited. We argue that this evidence-producing workflow is a critical missing evaluation axis for medical imaging agents. To study it, we introduce MedFlowBench, a full-study benchmark for VLM agents, together with MedOpenClaw, a controlled and replayable runtime in which agents operate medical imaging viewers such as 3D Slicer and QuPath. In each episode, an agent inspects a complete radiology study or whole-slide pathology image, returns a task answer, and submits structured evidence, including key slices, coordinates, regions of interest, or lesion-state fields. This evidence is automatically checked against withheld masks, annotations, and labels. Across evaluated models, final answer-only scoring gives an overly optimistic picture: when answers must also be supported by correct evidence, performance drops substantially on complex workflows. We further find that adding image-analysis tools does not by itself solve the problem. Tools help when they make a complex procedure simple and reliable, but agents still struggle when they must choose inputs, manage viewer state, and verify intermediate outputs over multiple steps. MedFlowBench exposes whether medical imaging agents can produce auditable evidence from complete studies, rather than plausible answers from selected images.

Weixiang Shen, Chengzhi Shen, Yanzhu Hu +12

Technical University of Munich (TUM) · TUM University Hospital · LMU Munich +6

Medical VLMsMedical Image BenchmarksAI Agent AuditingMedical Image Grounding
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Mar 25, 2026

Beyond Benchmarks: A Framework for Post Deployment Validation of CT Lung Nodule Detection AI

Background: Artificial intelligence (AI) assisted lung nodule detection systems are increasingly deployed in clinical settings without site-specific validation. Performance reported under benchmark conditions may not reflect real-world behavior when acquisition parameters differ from training data. Purpose: To propose and demonstrate a physics-guided framework for evaluating the sensitivity of a deployed lung nodule detection model to systematic variation in CT acquisition parameters. Methods: Twenty-one cases from the publicly available LIDC-IDRI dataset were evaluated using a MONAI RetinaNet model pretrained on LUNA16 (fold 0, no fine-tuning). Five imaging conditions were tested: baseline, 25% dose reduction, 50% dose reduction, 3 mm slice thickness, and 5 mm slice thickness. Dose reduction was simulated via image-domain Gaussian noise; slice thickness via moving average along the z-axis. Detection sensitivity was computed at a confidence threshold of 0.5 with a 15 mm matching criterion. Results: Baseline sensitivity was 45.2% (57/126 consensus nodules). Dose reduction produced slight degradation: 41.3% at 25% dose and 42.1% at 50% dose. The 5 mm slice thickness condition produced a marked drop to 26.2% - a 19 percentage point reduction representing a 42% relative decrease from baseline. This finding was consistent across confidence thresholds from 0.1 to 0.9. Per-case analysis revealed heterogeneous performance including two cases with complete detection failure at baseline. Conclusion: Slice thickness represents a more fundamental constraint on AI detection performance than image noise under the conditions tested. The proposed framework is reproducible, requires no proprietary scanner data, and is designed to serve as the basis for ongoing post-deployment QA in resource-constrained environment.

Daniel Soliman

Lung Nodule DetectionMedical Image AnalysisComputed TomographyMedical Image Anomaly DetectionMedical Image Benchmarks
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Mar 24, 2026

VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution

Recent advances in volumetric super-resolution (SR) have demonstrated strong performance in medical and scientific imaging, with transformer- and CNN-based approaches achieving impressive results even at extreme scaling factors. In this work, we show that much of this performance stems from training on downsampled data rather than real low-resolution scans. This reliance on downsampling is partly driven by the scarcity of paired high- and low-resolution 3D datasets. To address this, we introduce VoDaSuRe, a large-scale volumetric dataset containing paired high- and low-resolution scans. When training models on VoDaSuRe, we reveal a significant discrepancy: SR models trained on downsampled data produce substantially sharper predictions than those trained on real low-resolution scans, which smooth fine structures. Conversely, applying models trained on downsampled data to real scans preserves more structure but is inaccurate. Our findings suggest that current SR methods are overstated - when applied to real data, they do not recover structures lost in low-resolution scans and instead predict a smoothed average. We argue that progress in deep learning-based volumetric SR requires datasets with paired real scans of high complexity, such as VoDaSuRe. Our dataset and code are publicly available through: https://augusthoeg.github.io/VoDaSuRe/

August Leander Høeg, Sophia Wiinberg Bardenfleth, Hans Martin Kjer +3

Technical University of Denmark, Kgs. Lyngby, Denmark

Image Super-ResolutionMedical Image Super-ResolutionMedical Image BenchmarksSynthetic-to-Real Domain Adaptation
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Mar 22, 2026

CornOrb: A Multimodal Dataset of Orbscan Corneal Topography and Clinical Annotations for Keratoconus Detection

In this paper, we present CornOrb, a publicly accessible multimodal dataset of Orbscan corneal topography images and clinical annotations collected from patients in Algeria. The dataset comprises 1,454 eyes from 744 patients, including 889 normal eyes and 565 keratoconus cases. For each eye, four corneal maps are provided (axial curvature, anterior elevation, posterior elevation, and pachymetry), together with structured tabular data including demographic information and key clinical parameters such as astigmatism, maximum keratometry (Kmax), central and thinnest pachymetry, and anterior/posterior asphericity. All data were retrospectively acquired, fully anonymized, and pre-processed into standardized PNG and CSV formats to ensure direct usability for artificial intelligence research. This dataset represents one of the first large-scale Orbscan-based resources from Africa, specifically built to enable robust AI-driven detection and analysis of keratoconus using multimodal data. The data are openly available at Zenodo.

Mohammed El Amine Lazouni, Leila Ryma Lazouni, Zineb Aziza Elaouaber +8

Biomedical Engineering Laboratory, Abou Bakr Belkaid University, Tlemcen, Algeria · LIST Laboratory, M’Hamed Bougara Boumerdes University, Algeria · Lazouni Clinic, Tlemcen, Algeria +3

Medical Image BenchmarksMedical Image Analysis
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Mar 21, 2026

GOLDMARK: Governed Outcome-Linked Diagnostic Model Assessment Reference Kit

Computational biomarkers (CBs) are histopathology-derived patterns extracted from hematoxylin-eosin (H&E) whole-slide images (WSIs) using artificial intelligence (AI) to predict therapeutic response or prognosis. Recently, slide-level multiple-instance learning (MIL) with pathology foundation models (PFMs) has become the standard baseline for CB development. While these methods have improved predictive performance, computational pathology lacks standardized intermediate data formats, provenance tracking, checkpointing conventions, and reproducible evaluation metrics required for clinical-grade deployment. We introduce GOLDMARK (https://artificialintelligencepathology.org), a standardized benchmarking framework built on a curated TCGA cohort with clinically actionable OncoKB level 1-3 biomarker labels. GOLDMARK releases structured intermediate representations, including tile coordinate maps, per-slide feature embeddings from canonical PFMs, quality-control metadata, predefined patient-level splits, trained slide-level models, and evaluation outputs. Models are trained on TCGA and evaluated on an independent MSKCC cohort with reciprocal testing. Across 33 tumor-biomarker tasks, mean AUROC was 0.689 (TCGA) and 0.630 (MSKCC). Restricting to the eight highest-performing tasks yielded mean AUROCs of 0.831 and 0.801, respectively. These tasks correspond to established morphologic-genomic associations (e.g., LGG IDH1, COAD MSI/BRAF, THCA BRAF/NRAS, BLCA FGFR3, UCEC PTEN) and showed the most stable cross-site performance. Differences between canonical encoders were modest relative to task-specific variability. GOLDMARK establishes a shared experimental substrate for computational pathology, enabling reproducible benchmarking and direct comparison of methods across datasets and models.

Chad Vanderbilt, Gabriele Campanella, Siddharth Singi +10

Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA · Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Computational PathologyMedical Image BenchmarksPathology Foundation Models
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Mar 19, 2026

ReXInTheWild: A Unified Benchmark for Medical Photograph Understanding

Everyday photographs taken with ordinary cameras are already widely used in telemedicine and other online health conversations, yet no comprehensive benchmark evaluates whether vision-language models can interpret their medical content. Analyzing these images requires both fine-grained natural image understanding and domain-specific medical reasoning, a combination that challenges both general-purpose and specialized models. We introduce ReXInTheWild, a benchmark of 955 clinician-verified multiple-choice questions spanning seven clinical topics across 484 photographs sourced from the biomedical literature. When evaluated on ReXInTheWild, leading multimodal large language models show substantial performance variation: Gemini-3 achieves 78% accuracy, followed by Claude Opus 4.5 (72%) and GPT-5 (68%), while the medical specialist model MedGemma reaches only 37%. A systematic error analysis also reveals four categories of common errors, ranging from low-level geometric errors to high-level reasoning failures and requiring different mitigation strategies. ReXInTheWild provides a challenging, clinically grounded benchmark at the intersection of natural image understanding and medical reasoning. The dataset is available on HuggingFace.

Oishi Banerjee, Sung Eun Kim, Alexandra N. Willauer +9

Department of Biomedical Informatics, Harvard Medical School, Boston, MA · National Strategic Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea · Department of Medicine, Division of Gastroenterology, Massachusetts General Hospital, Boston, MA +3

Medical VLMsMedical Image BenchmarksMedical VQAMedical Image AnalysisMultimodal Model Evaluation
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Mar 19, 2026

Gastric-X: A Multimodal Multi-Phase Benchmark Dataset for Advancing Vision-Language Models in Gastric Cancer Analysis

Recent vision-language models (VLMs) have shown strong generalization and multimodal reasoning abilities in natural domains. However, their application to medical diagnosis remains limited by the lack of comprehensive and structured datasets that capture real clinical workflows. To advance the development of VLMs for clinical applications, particularly in gastric cancer, we introduce Gastric-X, a large-scale multimodal benchmark for gastric cancer analysis providing 1.7K cases. Each case in Gastric-X includes paired resting and dynamic CT scans, endoscopic image, a set of structured biochemical indicators, expert-authored diagnostic notes, and bounding box annotations of tumor regions, reflecting realistic clinical conditions. We systematically examine the capability of recent VLMs on five core tasks: Visual Question Answering (VQA), report generation, cross-modal retrieval, disease classification, and lesion localization. These tasks simulate critical stages of clinical workflow, from visual understanding and reasoning to multimodal decision support. Through this evaluation, we aim not only to assess model performance but also to probe the nature of VLM understanding: Can current VLMs meaningfully correlate biochemical signals with spatial tumor features and textual reports? We envision Gastric-X as a step toward aligning machine intelligence with the cognitive and evidential reasoning processes of physicians, and as a resource to inspire the development of next-generation medical VLMs.

Sheng Lu, Hao Chen, Rui Yin +3

Ruijin Hospital · University of Cambridge · Nanjing First Hospital +2

Medical VLMsMedical Image BenchmarksMedical DiagnosisMedical VQAMultimodal Model Evaluation
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Mar 19, 2026

Benchmarking CNN-based Models against Transformer-based Models for Abdominal Multi-Organ Segmentation on the RATIC Dataset

Accurate multi-organ segmentation in abdominal CT scans is essential for computer-aided diagnosis and treatment. While convolutional neural networks (CNNs) have long been the standard approach in medical image segmentation, transformer-based architectures have recently gained attention due to their ability to model long-range dependencies. In this study, we systematically benchmark the three hybrid transformer-based models UNETR, SwinUNETR, and UNETR++ against a strong CNN baseline, SegResNet, for volumetric multi-organ segmentation on the heterogeneous RATIC dataset. The dataset comprises 206 annotated CT scans from 23 institutions worldwide, covering five abdominal organs. All models were trained and evaluated under identical preprocessing and training conditions using the Dice Similarity Coefficient (DSC) as the primary metric. The results show that the CNN-based SegResNet achieves the highest overall performance, outperforming all hybrid transformer-based models across all organs. Among the transformer-based approaches, UNETR++ delivers the most competitive results, while UNETR demonstrates notably faster convergence with fewer training iterations. These findings suggest that, for small- to medium-sized heterogeneous datasets, well-optimized CNN architectures remain highly competitive and may outperform hybrid transformer-based designs.

Lukas Bayer, Sheethal Bhat, Andreas Maier

Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg

Medical Image SegmentationHybrid CNN-Transformer ArchitecturesConvolutional Neural NetworksMedical Image BenchmarksComputed Tomography
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Mar 19, 2026

MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning

Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling lesion implantation/removal that threatens clinical trust and safety. Existing defenses are inadequate for healthcare. Medical detectors are largely black-box, while MLLM-based explainers are typically post-hoc, lack medical expertise, and may hallucinate evidence on ambiguous cases. We present MedForge, a data-and-method solution for pre-hoc, evidence-grounded medical forgery detection. We introduce MedForge-90K, a large-scale benchmark of realistic lesion edits across 19 pathologies with expert-guided reasoning supervision via doctor inspection guidelines and gold edit locations. Building on it, MedForge-Reasoner performs localize-then-analyze reasoning, predicting suspicious regions before producing a verdict, and is further aligned with Forgery-aware GSPO to strengthen grounding and reduce hallucinations. Experiments demonstrate state-of-the-art detection accuracy and trustworthy, expert-aligned explanations.

Zhihui Chen, Kai He, Qingyuan Lei +4

National University of Singapore · The Chinese University of Hong Kong · Hunan University +2

Image Forgery DetectionExplainable Deepfake DetectionImage Forgery LocalizationMedical Image Benchmarks
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Mar 19, 2026

Mind the Rarities: Can Rare Skin Diseases Be Reliably Diagnosed via Diagnostic Reasoning?

Large vision-language models (LVLMs) demonstrate strong performance in dermatology; however, evaluating diagnostic reasoning for rare conditions remains largely unexplored. Existing benchmarks focus on common diseases and assess only final accuracy, overlooking the clinical reasoning process, which is critical for complex cases. We address this gap by constructing DermCase, a long-context benchmark derived from peer-reviewed case reports. Our dataset contains 26,030 multi-modal image-text pairs and 6,354 clinically challenging cases, each annotated with comprehensive clinical information and step-by-step reasoning chains. To enable reliable evaluation, we establish DermLIP-based similarity metrics that achieve stronger alignment with dermatologists for assessing differential diagnosis quality. Benchmarking 22 leading LVLMs exposes significant deficiencies across diagnosis accuracy, differential diagnosis, and clinical reasoning. Fine-tuning experiments demonstrate that instruction tuning substantially improves performance while Direct Preference Optimization (DPO) yields minimal gains. Systematic error analysis further reveals critical limitations in current models' reasoning capabilities.

Yang Liu, Jiyao Yang, Hongjin Zhao +10

Carnegie Mellon University · University of Alabama at Birmingham · Australian National University +3

Rare Disease DiagnosisMedical VLMsClinical ReasoningDermatologyMedical Image BenchmarksVLM Reasoning
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Mar 18, 2026

3D MRI-Based Alzheimer's Disease Classification Using Multi-Modal 3D CNN with Leakage-Aware Subject-Level Evaluation

Deep learning has become an important tool for Alzheimer's disease (AD) classification from structural MRI. Many existing studies analyze individual 2D slices extracted from MRI volumes, while clinical neuroimaging practice typically relies on the full three dimensional structure of the brain. From this perspective, volumetric analysis may better capture spatial relationships among brain regions that are relevant to disease progression. Motivated by this idea, this work proposes a multimodal 3D convolutional neural network for AD classification using raw OASIS 1 MRI volumes. The model combines structural T1 information with gray matter, white matter, and cerebrospinal fluid probability maps obtained through FSL FAST segmentation in order to capture complementary neuroanatomical information. The proposed approach is evaluated on the clinically labelled OASIS 1 cohort using 5 fold subject level cross validation, achieving a mean accuracy of 72.34% plus or minus 4.66% and a ROC AUC of 0.7781 plus or minus 0.0365. GradCAM visualizations further indicate that the model focuses on anatomically meaningful regions, including the medial temporal lobe and ventricular areas that are known to be associated with Alzheimer's related structural changes. To better understand how data representation and evaluation strategies may influence reported performance, additional diagnostic experiments were conducted on a slice based version of the dataset under both slice level and subject level protocols. These observations help provide context for the volumetric results. Overall, the proposed multimodal 3D framework establishes a reproducible subject level benchmark and highlights the potential benefits of volumetric MRI analysis for Alzheimer's disease classification.

Md Sifat, Sania Akter, Akif Islam +5

Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh · School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Japan · Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh

Alzheimer's Disease ClassificationMultimodal Classification3D CNNsMedical Image Benchmarks
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Mar 16, 2026

A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Images

Histopathology foundation models (HFMs), pretrained on large-scale cancer datasets, have advanced computational pathology. However, their applicability to non-cancerous chronic kidney disease remains underexplored, despite coexistence of renal pathology with malignancies such as renal cell and urothelial carcinoma. We systematically evaluate 11 publicly available HFMs across 11 kidney-specific downstream tasks spanning multiple stains (PAS, H&E, PASM, and IHC), spatial scales (tile and slide-level), task types (classification, regression, and copy detection), and clinical objectives, including detection, diagnosis, and prognosis. Tile-level performance is assessed using repeated stratified group cross-validation, while slide-level tasks are evaluated using repeated nested stratified cross-validation. Statistical significance is examined using Friedman test followed by pairwise Wilcoxon signed-rank testing with Holm-Bonferroni correction and compact letter display visualization. To promote reproducibility, we release an open-source Python package, kidney-hfm-eval, available at https://pypi.org/project/kidney-hfm-eval/ , that reproduces the evaluation pipelines. Results show moderate to strong performance on tasks driven by coarse meso-scale renal morphology, including diagnostic classification and detection of prominent structural alterations. In contrast, performance consistently declines for tasks requiring fine-grained microstructural discrimination, complex biological phenotypes, or slide-level prognostic inference, largely independent of stain type. Overall, current HFMs appear to encode predominantly static meso-scale representations and may have limited capacity to capture subtle renal pathology or prognosis-related signals. Our results highlight the need for kidney-specific, multi-stain, and multimodal foundation models to support clinically reliable decision-making in nephrology.

Harishwar Reddy Kasireddy, Patricio S. La Rosa, Akshita Gupta +12

Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA · Seed Production Innovation, Crop Science Division, Bayer Company, St. Louis, MO, USA · Division of Medicine – Quantitative Health, University of Florida, Gainesville, FL, USA +9

Pathology Foundation ModelsComputational PathologyMedical Image Benchmarks
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Mar 15, 2026

LUMINA: A Multi-Vendor Mammography Benchmark with Energy Harmonization Protocol

Publicly available full-field digital mammography (FFDM) datasets remain limited in size, clinical annotations, and vendor diversity, hindering the development of robust models. We introduce LUMINA, a curated, multi-vendor FFDM dataset that explicitly encodes acquisition energy and vendor metadata to capture clinically relevant appearance variations often overlooked in existing benchmarks. This dataset contains 1824 images from 468 patients (960 benign, 864 malignant), with pathology-confirmed labels, BI-RADS assessments, and breast-density annotations. LUMINA spans six acquisition systems and includes both high- and low-energy imaging styles, enabling systematic analysis of vendor- and energy-induced domain shifts. To address these variations, we propose a foreground-only pixel-space alignment method (''energy harmonization'') that maps images to a low-energy reference while preserving lesion morphology. We benchmark CNN and transformer models on three clinically relevant tasks: diagnosis (benign vs. malignant), BI-RADS classification, and density estimation. Two-view models consistently outperform single-view models. EfficientNet-B0 achieves an AUC of 93.54% for diagnosis, while Swin-T achieves the best macro-AUC of 89.43% for density prediction. Harmonization improves performance across architectures and produces more localized Grad-CAM responses. Overall, LUMINA provides (1) a vendor-diverse benchmark and (2) a model-agnostic harmonization framework for reliable and deployable mammography AI.

Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas +9

Department of Radiology, Northwestern University, Chicago, IL, USA · Department of Radiation Oncology, University of Health Sciences Prof. Dr. Cemil Tascioglu City Hospital, Istanbul, Turkey · Department of Radiology, Istanbul University, Istanbul, Turkey

Mammography Image ClassificationMedical Image HarmonizationMammographyMedical Image AnalysisMedical Image BenchmarksMedical Image Classification+1
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Mar 15, 2026

Medical Image Spatial Grounding with Semantic Sampling

Vision language models (VLMs) have shown significant promise in visual grounding for images as well as videos. In medical imaging research, VLMs represent a bridge between object detection and segmentation, and report understanding and generation. However, spatial grounding of anatomical structures in the three-dimensional space of medical images poses many unique challenges. In this study, we examine image modalities, slice directions, and coordinate systems as differentiating factors for vision components of VLMs, and the use of anatomical, directional, and relational terminology as factors for the language components. We then demonstrate that visual and textual prompting systems such as labels, bounding boxes, and mask overlays have varying effects on the spatial grounding ability of VLMs. To enable measurement and reproducibility, we introduce MIS-Ground, a benchmark that comprehensively tests a VLM for vulnerabilities against specific modes of Medical Image Spatial Grounding. We release MIS-Ground to the public at github.com/asy51/mis-ground. In addition, we present MIS-SemSam, a low-cost, inference-time, and model-agnostic optimization of VLMs that improves their spatial grounding ability with the use of Semantic Sampling. We find that MIS-SemSam improves the accuracy of Qwen3-VL-32B on MIS-Ground by 13.06%.

Andrew Seohwan Yu, Mohsen Hariri, Kunio Nakamura +3

Case Western Reserve University, Cleveland OH 44106, USA · Cleveland Clinic, Cleveland OH 44106, USA

Medical VLMsMedical Image GroundingMedical Image Benchmarks
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Mar 15, 2026

A comprehensive multimodal dataset and benchmark for ulcerative colitis scoring in endoscopy

Ulcerative colitis (UC) is a chronic mucosal inflammatory condition that places patients at increased risk of colorectal cancer. Colonoscopic surveillance remains the gold standard for assessing disease activity, and reporting typically relies on standardised endoscopic scoring metrics. The most widely used is the Mayo Endoscopic Score (MES), with some centres also adopting the Ulcerative Colitis Endoscopic Index of Severity (UCEIS). Both are descriptive assessments of mucosal inflammation (MES: 0 to 3; UCEIS: 0 to 8), where higher values indicate more severe disease. However, computational methods for automatically predicting these scores remain limited, largely due to the lack of publicly available expert-annotated datasets and the absence of robust benchmarking. There is also a significant research gap in generating clinically meaningful descriptions of UC images, despite image captioning being a well-established computer vision task. Variability in endoscopic systems and procedural workflows across centres further highlights the need for multi-centre datasets to ensure algorithmic robustness and generalisability. In this work, we introduce a curated multi-centre, multi-resolution dataset that includes expert-validated MES and UCEIS labels, alongside detailed clinical descriptions. To our knowledge, this is the first comprehensive dataset that combines dual scoring metrics for classification tasks with expert-generated captions describing mucosal appearance and clinically accepted reasoning for image captioning. This resource opens new opportunities for developing clinically meaningful multimodal algorithms. In addition to the dataset, we also provide benchmarking using convolutional neural networks, vision transformers, hybrid models, and widely used multimodal vision-language captioning algorithms.

Noha Ghatwary, Jiangbei Yue, Ahmed Elgendy +8

Computer Engineering Department, Arab Academy for Science and Technology,Smart Village, Giza, Egypt · AI in Medicine and Surgery group, School of Computer Science, University of Leeds, LS2 9JT, Leeds, United Kingdom · Department of Electrical and Computer Engineering, Queen’s University, Canada +5

Medical Image BenchmarksEndoscopyMedical VLMsMultimodal Model Evaluation
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Mar 14, 2026

Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space

Visual spatial intelligence is critical for medical image interpretation, yet remains largely unexplored in Multimodal Large Language Models (MLLMs) for 3D imaging. This gap persists due to a systemic lack of datasets featuring structured 3D spatial annotations beyond basic labels. In this study, we introduce an agentic pipeline that autonomously synthesizes spatial visual question-answering (VQA) data by orchestrating computational tools such as volume estimation and bounding boxes extraction with multi-agent collaboration and expert radiologist validation. We present SpatialMed, the first comprehensive benchmark for evaluating 3D spatial intelligence in medical MLLMs, comprising 31,253 question-answer pairs across multiple organs and tumor types. Our evaluations on 24 state-of-the-art MLLMs and extensive analyses reveal that current models lack robust spatial reasoning capabilities for medical imaging.

Quoc-Huy Trinh, Xi Ding, Yang Liu +7

3D Spatial ReasoningMultimodal Large Language ModelsMedical VQAMedical Image Benchmarks
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