Medical Image Benchmarks

Follow this topic Ask about this topic

Momentum

20 papers in the last four weeks, up 25% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 237

All topics

Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge
Jun 5, 2026Marc Aubreville, Jonas Ammeling, Sweta Banerjee +64Computational Pathology Domain Generalization
DaX: Learning General Pathology Representations Across Scales
Jun 5, 2026Bokai Zhao, Yiyang Zhang, Long Bai +3Pathology Foundation Models Medical Image Representation Learning
DAMO Academy, Alibaba Group · Institute of Automation, Chinese Academy of Sciences · Hupan Lab
MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models
Jun 4, 2026Ryan D'Cunha, Alejandro Lozano, Xiaoxiao Sun +17Medical Image Analysis Medical VLMs
Stanford University, Stanford, CA, USA · Instituto Tecnológico de Monterrey, Mexico · Monash University, Melbourne, VIC, Australia +4
A Vision-language Framework for Comparative Reasoning in Radiology
Jun 4, 2026Tengfei Zhang, Ziheng Zhao, Xiaoman Zhang +5Radiology VLMs Longitudinal Medical Image Analysis
University of Science and Technology of China · 3Shanghai Artificial Intelligence Laboratory · School of Artificial Intelligence, Shanghai Jiao Tong University +3
BreastGPT: A Multimodal Large Language Model for the Full Spectrum of Breast Cancer Clinical Routine
Jun 3, 2026Yang Liu, Jiajin Zhang, Danyang Tu +8Breast Cancer Medical Imaging
DAMO Academy, Alibaba Group · Zhejiang University · West China Hospital +1
CoralBay: A Self-Supervised CT Foundation Model
Jun 2, 2026Ioannis Gatopoulos, Nicolas Känzig, Sebastian Otálora +1Medical Imaging Foundation Models 3D Representation Learning
Automated Report-Derived Oncology VQA Benchmark for Evaluating Vision-Language Models on 3D Medical Imaging
Jun 1, 2026Bo Liu, Hanxue Gu, Xiangru Li +6VLM Evaluation Medical Imaging
UCSF–UC Berkeley Joint Graduate Program in Bioengineering · Department of Radiology, UCSF · Department of Radiation Oncology, UCSF
A Systematic Benchmark of Intraoperative Ultrasound-to-MR Synthesis for Brain Tumour Surgery
May 30, 2026Olga Esteban-Sinovas, Santiago Cepeda, Ignacio Arrese +1Ultrasound Imaging Medical Image Synthesis
Department of Neurosurgery, Neurovascular Unit Río Hortega University Hospital, Valladolid, Spain · Specialized Group in Biomedical Imaging and Computational Analysis (GEIBAC) Instituto de Investigación Biosanitaria de Valladolid (IBioVALL), Valladolid, Spain
ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training
May 30, 2026Rongsheng Wang, Fenghe Tang, Zihang Jiang +10Medical VLMs Medical Image Grounding
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei, Anhui, China 230026 · Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE) Lab, YRD-RIGHT, USTC Suzhou Institute for Advanced Research, Suzhou, Jiangsu, China 215123 · Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology, Suzhou, Jiangsu, China 215123 +5
Beyond Accuracy: Evaluating Efficiency, Robustness and Explainability in Deep Learning for Malaria Diagnosis
May 29, 2026Olivier Kanamugire, Kerol DjoumessiEfficient Neural Network Inference Image Corruption Robustness
African Institute for Mathematical Sciences, Kigali, Rwanda · Hertie Institute for AI in Brain Health, University of Tübingen, Germany
Fairness Beyond Demographics: Optimizing Performance Across Appearance-Based Hidden Cohorts in Medical Imaging
May 28, 2026Milad Masroor, Cuong Nguyen, Kevin Wells +1Medical Image Analysis Algorithmic Fairness
Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations
May 28, 2026Zixian Su, Hongkai Zhang, Fan Gao +12Magnetic Resonance Imaging Medical VLMs
Beijing Academy of Artificial Intelligence · Beijing Anzhen Hospital · Beihang University +1
Pocket-Dentist: On-Device Dental Image Understanding via Efficient Multimodal Large Language Models
May 28, 2026Kai Bian, Xucheng Guo, Bin Chen +4Medical Imaging Efficient VLM Inference
The University of Auckland, New Zealand · Shandong University, China · The University of Melbourne, Australia
MeniOmni: A Structured Multimodal Benchmark for Holistic Meniscus Injury Assessment
May 27, 2026Shurui Xu, Siqi Yang, Weiping Ding +4Radiology Report Generation Medical Image Benchmarks
School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, UK · Radiology Department, Affiliated Nantong Clinical College of Nantong University, Nantong First People’s Hospital, School of Clinical Medicine, Nantong University, Nantong, Jiangsu, China · School of Artificial Intelligence and Computer Science, Nantong University, Nantong, China +5
Benchmarking Ultrasound Foundation Models for Fetal Plane Classification
May 27, 2026Leya Barrientos, Yuexi Du, Nicha C. DvornekMedical Imaging Foundation Models Medical Image Classification
Radiology & Biomedical Imaging, Yale School of Medicine, USA · Department of Biomedical Engineering, Yale University, USA
Cesarean Scar Defect Segmentation in Transvaginal Ultrasound Images: a Dataset and Benchmark
May 26, 2026Yuan Tian, Yue Li, Wei Xia +10Ultrasound Image Segmentation Ultrasound Imaging
Department of Obstetrics and Gynecology, International Peace Maternity and Child Health Hospital affiliated to Shanghai Jiao Tong University School of Medicine, 910 Hengshan Road, Xuhui District, 200030, Shanghai, China. · School of Computer Science, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo, 315100, Zhejiang, China. · School of Computer Science, University of Nottingham, University Park, Nottingham, NG7 2RD, UK. +4
Benchmarking Convolutional, Transformer, Hybrid, and Vision Language Models for Multi Disease Retinal Screening
May 25, 2026Durjoy Dey, Aymane Ajbar, Yuhong YanHybrid CNN-Transformer Architectures Vision Transformer
Department of Computer Science and Software Engineering Concordia University Montreal, QC, Canada · Ebovir Biotechnologie Inc. Montreal, QC, Canada
Benchmarking Pathology Foundation Models for Spatial Domain Understanding
May 25, 2026Bokai Zhao, Yiyang Zhang, Yuanchi Zhu +6Pathology Foundation Models Medical Image Benchmarks
School of Artificial Intelligence, University of Chinese Academy of Sciences. · Brainnetome Center, Institute of Automation, Chinese Academy of Sciences. · Beijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences. +2
Which Anatomy Matters Under Limited Labels? A Data-Efficient Anatomy-Aware Benchmark for Cardiac Pathology Prediction
May 25, 2026Himanshu SinghHealthcare Medical Image Analysis
Computing & Artificial Intelligence Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark
May 25, 2026Yuanye Liu, Nannan Shi, Zhejia Zhang +20Medical Image Classification Medical Image Benchmarks
School of Data Science, Fudan University, Shanghai, China · Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China · Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA +12
Are We Overconfident in Models and Results for Semi-Supervised 3D Medical Image Segmentation?
May 25, 2026Jun Li, Ziwei QinBenchmark Contamination Semi-Supervised Medical Image Segmentation
Institute of Systems Science and Technology, School of Electrical Engineering, Southwest Jiaotong University, China.
Methodology for Creating a Clinically Verified Dermoscopic Image Dataset
May 24, 2026Kozachok Elena SergeevnaTraining Data Curation Medical Image Benchmarks
Ivannikov Institute for System Programming of RAS, Moscow, Russia
MedicalRec: Medical recommender system for image classification without retraining
May 23, 2026Roghayeh Taghavi, Aysa Hasanazde Bashkandi, Amir Ali Bengari +5Model Selection Healthcare
Med-R2: An Adversarial Benchmark for Evidence-Grounded Reasoning in Medical VLMs
May 23, 2026Wen Ma, Fucheng Niu, Zhiting Fan +3Adversarial Attacks on VLMs Healthcare
Zhejiang University, Zhejiang, China · Guangdong Institute of Intelligence Science and Technology
EchoVQA: Enabling Conversational Assistance for Point-of-Care Cardiac Ultrasound
May 22, 2026Filippos Bellos, Yutong Li, Jessie N Dong +6Medical VQA Echocardiography
University of Michigan · University of Pennsylvania · Independent Scientist
DDX-TRACE: A Benchmark for Medical Diagnostic Trajectories in VLMs
May 22, 2026Jiazhen Pan, Weixiang Shen, Jun Li +7Medical Imaging Radiology VLMs
Technical University of Munich (TUM) · TUM University Hospital · Munich Center for Machine Learning (MCML) +3
Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking
May 22, 2026Yannick Kirchhoff, Maximilian Rokuss, Daniel Philipp Mertens +5Interactive Medical Image Segmentation Medical Imaging
German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany · Faculty of Mathematics and Computer Science, Heidelberg University, Germany · HIDSS4Health – Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany +3
RoboSurg-VQA: A Multimodal Benchmark for Surgical Segmentation-Aware Visual Question Answering
May 21, 2026Chengyi Zhang, Zi Ye, Ziyang WangMedical VQA Medical Image Benchmarks
Swansea University, UK · Maynooth University, Ireland · Aston University, UK
Rethinking Noise-Robust Training for Frozen Vision Foundation Models: A Cross-Dataset Benchmark with a Case Study of Small-Loss Failure
May 21, 2026Zitong Li, Haoyu WangVision Foundation Model Adaptation Noisy-Label Learning
Department of Biostatistics and Health Informatics · King's College London
Towards Clinically Interpretable Ophthalmic VQA via Spatially-Grounded Lesion Evidence
May 21, 2026Xingyue Wang, Bo Liu, Meng Wang +4Medical VQA Fundus Imaging
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China · The Hong Kong Polytechnic University, Hong Kong S.A.R., China. · National University of Singapore, Singapore. +2

Jun 5, 2026, cs.CVJ/K move · Enter open · S save

Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge

Marc Aubreville, Jonas Ammeling, Sweta Banerjee, Viktoria Weiss+63

Computational Pathology Domain Generalization Medical Image Anomaly Detection Oncology Medical Image Benchmarks Whole-Slide Image Analysis

Open paper AskPDF arXiv

Automated mitosis detection is a well-established task in computational pathology. While previous benchmarks focused on scanner-induced domain shift, clinical "real-world" application requires models to be robust across the vast variance to be expected in the histological landscape. The MItosis DOmain Generalization (MIDOG) 2025 challenge was designed to evaluate algorithmic performance across unprecedented biological and contextual diversity. We curated a test dataset of 365 cases, encompassing 12 distinct human, canine and feline tumor types, digitized across multiple scanning platforms. Moving beyond hand-selected hotspots, the challenge required detection also in random tissue areas (representative of the whole slide detection situation) and challenging areas (areas rich in hard negatives). In the second track, we introduced the classification of atypical mitotic figures (AMFs). There were 18 teams submitting to the detection track, with F1 scores ranging up to 0.740. In the AMF detection track, we had 21 submissions with balanced accuracy values up to 0.908. Our analysis reveals that while most models perform reliably in traditional hotspots, significant performance degradation occurs in challenging ROIs, where false positive rates tripled. Furthermore, performance varied significantly across the 12 tumor types, highlighting "blind spots" in current state-of-the-art architectures when encountering rare or highly pleomorphic malignancies. Moreover, we evaluated the effectiveness of ensembling and found a mean increases of 1.5 and 1.3 percentage points in F1 score and balanced accuracy, respectively. In contrast, TTA showed no relevant improvement. MIDOG 2025 demonstrates that "in the wild" mitosis detection remains a significant hurdle. The transition from hotspot-only evaluation to a multi-contextual framework provides a more realistic proxy for clinical reliability.

Momentum

Related topics

Latest papers 237