Explainable Medical Image Analysis

Ask about this topic

Overview

This field studies how AI systems analyze medical images and how clinicians can inspect the evidence behind predictions, segmentations, and reports. Approaches include post-hoc saliency and perturbation methods, prototypes and concept bottlenecks, interpretable radiomic features, counterfactuals, and vision-language systems that link findings to clinical concepts or anatomical regions. Methods are tested across tasks such as chest X-ray diagnosis, retinal grading, tumor segmentation, and whole-slide pathology, with growing attention to external-site generalization and clinician assessment. A central challenge is that attractive explanations may not faithfully reflect what drives a model, while explanation stability, clinical usefulness, and predictive performance can vary across architectures, methods, and datasets.

Latest papers

CardsList

256 papers23 papers in the last four weeks, up 10% on the four weeks before. 0.1% of all new papers.

Mar 17, 2026

Topology-Guided Biomechanical Profiling: A White-Box Framework for Opportunistic Screening of Spinal Instability on Routine CT

Routine oncologic computed tomography (CT) presents an ideal opportunity for screening spinal instability, yet prophylactic stabilization windows are frequently missed due to the complex geometric reasoning required by the Spinal Instability Neoplastic Score (SINS). Automating SINS is fundamentally hindered by metastatic osteolysis, which induces topological ambiguity that confounds standard segmentation and black-box AI. We propose Topology-Guided Biomechanical Profiling (TGBP), an auditable white-box framework decoupling anatomical perception from structural reasoning. TGBP anchors SINS assessment on two deterministic geometric innovations: (i) canal-referenced partitioning to resolve posterolateral boundary ambiguity, and (ii) context-aware morphometric normalization via covariance-based oriented bounding boxes (OBB) to quantify vertebral collapse. Integrated with auxiliary radiomic and large language model (LLM) modules, TGBP provides an end-to-end, interpretable SINS evaluation. Validated on a multi-center, multi-cancer cohort (N=482N=482), TGBP achieved 90.2% accuracy in 3-tier stability triage. In a blinded reader study (N=30N=30), TGBP significantly outperformed medical oncologists on complex structural features (κ=0.857κ=0.857 vs.\ 0.5700.570) and prevented compounding errors in Total Score estimation (κ=0.625κ=0.625 vs.\ 0.2070.207), democratizing expert-level opportunistic screening.

Zanting Ye, Xuanbin Wu, Guoqing Zhong +9

Southern Medical University, Guangzhou, China · Westlake University, Hangzhou, China · The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China +5

Explainable Medical Image Analysis
AskPDF
Mar 15, 2026

Histo-MExNet: A Unified Framework for Real-World, Cross-Magnification, and Trustworthy Breast Cancer Histopathology

Accurate and reliable histopathological image classification is essential for breast cancer diagnosis. However, many deep learning models remain sensitive to magnification variability and lack interpretability. To address these challenges, we propose Histo-MExNet, a unified framework designed for scaleinvariant and uncertainty-aware classification. The model integrates DenseNet, ConvNeXt, and EfficientNet backbones within a gated multi-expert architecture, incorporates a prototype learning module for example-driven interpretability, and applies physics-informed regularization to enforce morphology preservation and spatial coherence during feature learning. Monte Carlo Dropout is used to quantify predictive uncertainty. On the BreaKHis dataset, Histo-MExNet achieves 96.97% accuracy under multi-magnification training and demonstrates improved generalization to unseen magnification levels compared to single-expert models, while uncertainty estimation helps identify out-of-distribution samples and reduce overconfident errors, supporting a balanced combination of accuracy, robustness, and interpretability for clinical decision support.

Enam Ahmed Taufika, Md Ahasanul Arafatha, Abhijit Kumar Ghoshb +2

European University of Bangladesh, Department of Computer Science and Engineering, Dhaka, Bangladesh · BRAC University, Department of Computer Science and Engineering, Dhaka, Bangladesh · Ochsner Center for Outcomes Research, Ochsner Research, Ochsner Clinic Foundation, New Orleans, LA, 70121, USA

Histopathology Image ClassificationExplainable Medical Image Analysis
AskPDF
Mar 14, 2026

EviAgent: Evidence-Driven Agent for Radiology Report Generation

Automated radiology report generation holds immense potential to alleviate the heavy workload of radiologists. Despite the formidable vision-language capabilities of recent Multimodal Large Language Models (MLLMs), their clinical deployment is severely constrained by inherent limitations: their "black-box" decision-making renders the generated reports untraceable due to the lack of explicit visual evidence to support the diagnosis, and they struggle to access external domain knowledge. To address these challenges, we propose the Evidence-driven Radiology Report Generation Agent (EviAgent). Unlike opaque end-to-end paradigms, EviAgent coordinates a transparent reasoning trajectory by breaking down the complex generation process into granular operational units. We integrate multi-dimensional visual experts and retrieval mechanisms as external support modules, endowing the system with explicit visual evidence and high-quality clinical priors. Extensive experiments on MIMIC-CXR, CheXpert Plus, and IU-Xray datasets demonstrate that EviAgent outperforms both large-scale generalist models and specialized medical models, providing a robust and trustworthy solution for automated radiology report generation.

Tuoshi Qi, Shenshen Bu, Yingfei Xiang +1

School of Computer Science and Engineering, Sun Yat-sen University · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · Sangfor Technologies, Shenzhen, Guangdong, China

Radiology Report GenerationMedical VLMsExplainable Medical Image AnalysisAgentic RAG
AskPDF
Mar 12, 2026

Automated Detection of Malignant Lesions in the Ovary Using Deep Learning Models and XAI

The unrestrained proliferation of cells that are malignant in nature is cancer. In recent times, medical professionals are constantly acquiring enhanced diagnostic and treatment abilities by implementing deep learning models to analyze medical data for better clinical decision, disease diagnosis and drug discovery. A majority of cancers are studied and treated by incorporating these technologies. However, ovarian cancer remains a dilemma as it has inaccurate non-invasive detection procedures and a time consuming, invasive procedure for accurate detection. Thus, in this research, several Convolutional Neural Networks such as LeNet-5, ResNet, VGGNet and GoogLeNet/Inception have been utilized to develop 15 variants and choose a model that accurately detects and identifies ovarian cancer. For effective model training, the dataset OvarianCancer&SubtypesDatasetHistopathology from Mendeley has been used. After constructing a model, we utilized Explainable Artificial Intelligence (XAI) models such as LIME, Integrated Gradients and SHAP to explain the black box outcome of the selected model. For evaluating the performance of the model, Accuracy, Precision, Recall, F1-Score, ROC Curve and AUC have been used. From the evaluation, it was seen that the slightly compact InceptionV3 model with ReLu had the overall best result achieving an average score of 94% across all the performance metrics in the augmented dataset. Lastly for XAI, the three aforementioned XAI have been used for an overall comparative analysis. It is the aim of this research that the contributions of the study will help in achieving a better detection method for ovarian cancer.

Md. Hasin Sarwar Ifty, Nisharga Nirjan, Labib Islam +4

Dept. of Computer Science and Engr. BRAC University Dhaka, Bangladesh

Histopathology Image ClassificationComputational PathologyConvolutional Neural NetworksExplainable Medical Image Analysis
AskPDF
Mar 12, 2026

DeepHistoViT: An Interpretable Vision Transformer Framework for Histopathological Cancer Classification

Histopathology remains the gold standard for cancer diagnosis because it provides detailed cellular-level assessment of tissue morphology. However, manual histopathological examination is time-consuming, labour-intensive, and subject to inter-observer variability, creating a demand for reliable computer-assisted diagnostic tools. Recent advances in deep learning, particularly transformer-based architectures, have shown strong potential for modelling complex spatial dependencies in medical images. In this work, we propose DeepHistoViT, a transformer-based framework for automated classification of histopathological images. The model employs a customized Vision Transformer architecture with an integrated attention mechanism designed to capture fine-grained cellular structures while improving interpretability through attention-based localization of diagnostically relevant regions. The framework is evaluated on three publicly available histopathology datasets covering lung cancer, colon cancer, and acute lymphoblastic leukaemia. Experimental results demonstrate state-of-the-art performance across all datasets, with classification accuracy, precision, recall, F1-score, and ROC-AUC reaching 100 percent on the lung and colon cancer datasets, and 99.85 percent, 99.84 percent, 99.86 percent, 99.85 percent, and 99.99 percent respectively on the acute lymphoblastic leukaemia dataset. All performance metrics are reported with 95 percent confidence intervals. These results highlight the effectiveness of transformer-based architectures for histopathological image analysis and demonstrate the potential of DeepHistoViT as an interpretable computer-assisted diagnostic tool to support pathologists in clinical decision-making.

Ravi Mosalpuri, Mohammed Abdelsamea, Ahmed Karam Eldaly

Histopathology Image ClassificationComputational PathologyVision TransformerExplainable Medical Image Analysis
AskPDF
Mar 11, 2026

UltrasoundAgents: Hierarchical Multi-Agent Evidence-Chain Reasoning for Breast Ultrasound Diagnosis

Breast ultrasound diagnosis typically proceeds from global lesion localization to local sign assessment and then evidence integration to assign a BI-RADS category and determine benignity or malignancy. Many existing methods rely on end-to-end prediction or provide only weakly grounded evidence, which can miss fine-grained lesion cues and limit auditability and clinical review. To align with the clinical workflow and improve evidence traceability, we propose a hierarchical multi-agent framework, termed UltrasoundAgents. A main agent localizes the lesion in the full image and triggers a crop-and-zoom operation. A sub-agent analyzes the local view and predicts four clinically relevant attributes, namely echogenicity pattern, calcification, boundary type, and edge (margin) morphology. The main agent then integrates these structured attributes to perform evidence-based reasoning and output the BI-RADS category and the malignancy prediction, while producing reviewable intermediate evidence. Furthermore, hierarchical multi-agent training often suffers from error propagation, difficult credit assignment, and sparse rewards. To alleviate this and improve training stability, we introduce a decoupled progressive training strategy. We first train the attribute agent, then train the main agent with oracle attributes to learn robust attribute-based reasoning, and finally apply corrective trajectory self-distillation with spatial supervision to build high-quality trajectories for supervised fine-tuning, yielding a deployable end-to-end policy. Experiments show consistent gains over strong vision-language baselines in diagnostic accuracy and attribute agreement, together with structured evidence and traceable reasoning.

Yali Zhu, Kang Zhou, Dingbang Wu +1

Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China · Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong

Explainable Medical Image AnalysisMedical Image ClassificationUltrasound ImagingMulti-Agent Reasoning
AskPDF
Mar 9, 2026

Vision-Language Models Encode Clinical Guidelines for Concept-Based Medical Reasoning

Concept Bottleneck Models (CBMs) are a prominent framework for interpretable AI that map learned visual features to a set of meaningful concepts for task-specific downstream predictions. Their sequential structure enhances transparency by connecting model predictions to the underlying concepts that support them. In medical imaging, where transparency is essential, CBMs offer an appealing foundation for explainable model design. However, discrete concept representations often overlook broader clinical context such as diagnostic guidelines and expert heuristics, reducing reliability in complex cases. We propose MedCBR, a concept-based reasoning framework that integrates clinical guidelines with vision-language and reasoning models. Labeled clinical descriptors are transformed into guideline-conformant text, and a concept-based model is trained with a multitask objective combining multimodal contrastive alignment, concept supervision, and diagnostic classification to jointly ground image features, concepts, and pathology. A reasoning model then converts these predictions into structured clinical narratives that explain the diagnosis, emulating expert reasoning based on established guidelines. MedCBR achieves superior diagnostic and concept-level performance, with AUROCs of 94.2% on ultrasound and 84.0% on mammography. Further experiments on non-medical datasets achieve 86.1% accuracy. Our framework enhances interpretability and forms an end-to-end bridge from medical image analysis to decision-making.

Mohamed Harmanani, Bining Long, Zhuoxin Guo +6

Queen’s University · Vector Institute · McMaster University +1

Explainable Medical Image AnalysisMedical ReasoningMedical VLMsConcept Bottleneck ModelsMedical Image Classification
AskPDF
Mar 8, 2026

Interpretable Aneurysm Classification via 3D Concept Bottleneck Models: Integrating Morphological and Hemodynamic Clinical Features

We are concerned with the challenge of reliably classifying and assessing intracranial aneurysms using deep learning without compromising clinical transparency. While traditional black-box models achieve high predictive accuracy, their lack of inherent interpretability remains a significant barrier to clinical adoption and regulatory approval. Explainability is paramount in medical modeling to ensure that AI-driven diagnoses align with established neurosurgical principles. Unlike traditional eXplainable AI (XAI) methods -- such as saliency maps, which often provide post-hoc, non-causal visual correlations -- Concept Bottleneck Models (CBMs) offer a robust alternative by constraining the model's internal logic to human-understandable clinical indices. In this article, we propose an end-to-end 3D Concept Bottleneck framework that maps high-dimensional neuroimaging features to a discrete set of morphological and hemodynamic concepts for aneurysm identification. We implemented this pipeline using a pre-trained 3D ResNet-34 backbone and a 3D DenseNet-121 to extract features from CTA volumes, which were subsequently processed through a soft bottleneck layer representing human-interpretable clinical concepts. The model was optimized using a joint-loss function to balance diagnostic focal loss and concept mean squared error (MSE), validated via stratified five-fold cross-validation. Our results demonstrate a peak task classification accuracy of 93.33% +/- 4.5% for the ResNet-34 architecture and 91.43% +/- 5.8% for the DenseNet-121 model. Furthermore, the implementation of 8-pass Test-Time Augmentation (TTA) yielded a robust mean accuracy of 88.31%, ensuring diagnostic stability during inference. By maintaining an accuracy-generalization gap of less than 0.04, this framework proves that high predictive performance can be achieved without sacrificing interpretability.

Toqa Khaled, Ahmad Al-Kabbany

Biomedical Sciences Program, School of Sciences, Zewail City of Science and Technology · Multimedia Interaction and Communication Lab, Arab Academy for Science and Technology · Multimedia Interaction and Communication Lab, Wearables, Biosensing, and Biosignal Processing Research lab, Arab Academy for Science and Technology

Explainable Medical Image AnalysisMedical Image ClassificationConcept Bottleneck Models3D Medical Imaging
AskPDF
Mar 7, 2026

Class Visualizations and Activation Atlases for Enhancing Interpretability in Deep Learning-Based Computational Pathology

The rapid adoption of transformer-based models in computational pathology has enabled prediction of molecular and clinical biomarkers from H&E whole-slide images, yet interpretability has not kept pace with model complexity. While attribution- and generative-based methods are common, feature visualization approaches such as class visualizations (CVs) and activation atlases (AAs) have not been systematically evaluated for these models. We developed a visualization framework and assessed CVs and AAs for a transformer-based foundation model across tissue and multi-organ cancer classification tasks with increasing label granularity. Four pathologists annotated real and generated images to quantify inter-observer agreement, complemented by attribution and similarity metrics. CVs preserved recognizability for morphologically distinct tissues but showed reduced separability for overlapping cancer subclasses. In tissue classification, agreement decreased from Fleiss k = 0.75 (scans) to k = 0.31 (CVs), with similar trends in cancer subclass tasks. AAs revealed layer-dependent organization: coarse tissue-level concepts formed coherent regions, whereas finer subclasses exhibited dispersion and overlap. Agreement was moderate for tissue classification (k = 0.58), high for coarse cancer groupings (k = 0.82), and low at subclass level (k = 0.11). Atlas separability closely tracked expert agreement on real images, indicating that representational ambiguity reflects intrinsic pathological complexity. Attribution-based metrics approximated expert variability in low-complexity settings, whereas perceptual and distributional metrics showed limited alignment. Overall, concept-level feature visualization reveals structured morphological manifolds in transformer-based pathology models and provides a framework for expert-centered interrogation of learned representations across label granularities.

Marco Gustav, Fabian Wolf, Christina Glasner +6

Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany · Institute of Pathology, University Medical Center Mainz, Mainz, Germany · Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany +4

Feature VisualizationExplainable Medical Image AnalysisComputational PathologyTransformer InterpretabilityHistopathology Image Classification
AskPDF
Mar 6, 2026

Vessel-Aware Deep Learning for OCTA-Based Detection of AMD

Age-related macular degeneration (AMD) is characterized by early micro-vascular alterations that can be captured non-invasively using optical coherence tomography angiography (OCTA), yet most deep learning (DL) models rely on global features and fail to exploit clinically meaningful vascular biomarkers. We introduce an external multiplicative attention framework that incorporates vessel-specific tortuosity maps and vasculature dropout maps derived from arteries, veins, and capillaries. These biomarker maps are generated from vessel segmentations and smoothed across multiple spatial scales to highlight coherent patterns of vascular remodeling and capillary rarefaction. Tortuosity reflects abnormalities in vessel geometry linked to impaired auto-regulation, while dropout maps capture localized perfusion deficits that precede structural retinal damage. The maps are fused with the OCTA projection to guide a deep classifier toward physiologically relevant regions. Arterial tortuosity provided the most consistent discriminative value, while capillary dropout maps performed best among density-based variants, especially at larger smoothing scales. Our proposed method offers interpretable insights aligned with known AMD pathophysiology.

Margalit G. Mitzner, Moinak Bhattacharya, Zhilin Zou +2

Stony Brook University, Stony Brook, NY, USA

Medical Image AnalysisExplainable Medical Image AnalysisOptical Coherence TomographyAttention Mechanisms
AskPDF
Mar 5, 2026

Interpretable Motion Artificat Detection in structural Brain MRI

Automated quality assessment of structural brain MRI is an important prerequisite for reliable neuroimaging analysis, but yet remains challenging due to motion artifacts and poor generalization across acquisition sites. Existing approaches based on image quality metrics (IQMs) or deep learning either requires extensive preprocessing, which incurs high computational cost, or poor generalization to unseen data. In this work, we propose a lightweight and interpretable framework for detecting motion related artifacts in T1 weighted brain MRI by extending the Discriminative Histogram of Gradient Magnitude (DHoGM) to a three dimensional space. The proposed method integrates complementary slice-level (2D) and volume-level (3D) DHoGM features through a parallel decision strategy, capturing both localized and global motion-induced degradation. Volumetric analysis is performed using overlapping 3D cuboids to achieve comprehensive spatial coverage while maintaining computational efficiency. A simple threshold-based classifier and a low parameter multilayer perceptron are used, which results in a model with only 209 trainable parameters. Our method was evaluated on the MR-ART and ABIDE datasets under both seen-site and unseen-site conditions. Experimental results demonstrate strong performance, achieving up to 94.34% accuracy the in domain evaluation and 89% accuracy on unseen sites, while almost completely avoiding false acceptance of poor-quality scans. Ablation studies confirms the complementary benefits of combining 2D and 3D features. Overall, the proposed approach offers an effective, efficient, and robust solution for automated MRI quality check, with strong potential for integration into large scale clinical and research workflows.

Naveetha Nithianandam, Prabhjot Kaur, Anil Kumar Sao

Indian Institute of Technology, Madras · Boston Childrens Hospital, Harvard Medical School, Boston, USA · Indian Institute of Technology, Bhilai

Image Quality AssessmentBrain MRIExplainable Medical Image Analysis
AskPDF
Mar 5, 2026

Adaptive Prototype-based Interpretable Grading of Prostate Cancer

Prostate cancer being one of the frequently diagnosed malignancy in men, the rising demand for biopsies places a severe workload on pathologists. The grading procedure is tedious and subjective, motivating the development of automated systems. Although deep learning has made inroads in terms of performance, its limited interpretability poses challenges for widespread adoption in high-stake applications like medicine. Existing interpretability techniques for prostate cancer classifiers provide a coarse explanation but do not reveal why the highlighted regions matter. In this scenario, we propose a novel prototype-based weakly-supervised framework for an interpretable grading of prostate cancer from histopathology images. These networks can prove to be more trustworthy since their explicit reasoning procedure mirrors the workflow of a pathologist in comparing suspicious regions with clinically validated examples. The network is initially pre-trained at patch-level to learn robust prototypical features associated with each grade. In order to adapt it to a weakly-supervised setup for prostate cancer grading, the network is fine-tuned with a new prototype-aware loss function. Finally, a new attention-based dynamic pruning mechanism is introduced to handle inter-sample heterogeneity, while selectively emphasizing relevant prototypes for optimal performance. Extensive validation on the benchmark PANDA and SICAP datasets confirms that the framework can serve as a reliable assistive tool for pathologists in their routine diagnostic workflows.

Riddhasree Bhattacharyya, Pallabi Dutta, Sushmita Mitra

Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108

Prostate Cancer GradingHistopathology Image ClassificationComputational PathologyExplainable Medical Image AnalysisPrototype-Based ClassificationWeakly Supervised Learning
AskPDF
Mar 5, 2026

Missingness Bias Calibration in Feature Attribution Explanations

Popular explanation methods often produce unreliable feature importance scores due to missingness bias, a systematic distortion that arises when models are probed with ablated, out-of-distribution inputs. Existing solutions treat this as a deep representational flaw that requires expensive retraining or architectural modifications. In this work, we challenge this assumption and show that missingness bias can be effectively treated as a superficial artifact of the model's output space. We introduce MCal, a lightweight post-hoc method that corrects this bias by fine-tuning a simple linear head on the outputs of a frozen base model. Surprisingly, we find this simple correction consistently reduces missingness bias and is competitive with, or even outperforms, prior heavyweight approaches across diverse medical benchmarks spanning vision, language, and tabular domains.

Shailesh Sridhar, Anton Xue, Eric Wong

University of Pennsylvania · University of Texas at Austin

Feature AttributionPerturbation-Based Feature AttributionExplainable Medical Image Analysis
AskPDF
Mar 4, 2026

CRESTomics: Analyzing Carotid Plaques in the CREST-2 Trial with a New Additive Classification Model

Accurate characterization of carotid plaques is critical for stroke prevention in patients with carotid stenosis. We analyze 500 plaques from CREST-2, a multi-center clinical trial, to identify radiomics-based markers from B-mode ultrasound images linked with high-risk. We propose a new kernel-based additive model, combining coherence loss with group-sparse regularization for nonlinear classification. Group-wise additive effects of each feature group are visualized using partial dependence plots. Results indicate our method accurately and interpretably assesses plaques, revealing a strong association between plaque texture and clinical risk.

Pranav Kulkarni, Brajesh K. Lal, Georges Jreij +11

University of Maryland Institute for Health Computing · University of Maryland, College Park · University of Maryland School of Medicine +3

Medical Image ClassificationExplainable Medical Image AnalysisRadiomicsKernel Methods
AskPDF
Mar 3, 2026

Beyond Anatomy: Explainable ASD Classification from rs-fMRI via Functional Parcellation and Graph Attention Networks

Anatomical brain parcellations dominate rs-fMRI-based Autism Spectrum Disorder (ASD) classification, yet their rigid boundaries may fail to capture the idiosyncratic connectivity patterns that characterise ASD. We present a graph-based deep learning framework comparing anatomical (AAL, 116 ROIs) and functionally-derived (MSDL, 39 ROIs) parcellation strategies on the ABIDE I dataset. Our FSL preprocessing pipeline handles multi-site heterogeneity across 400 balanced subjects, with site-stratified 70/15/15 splits to prevent data leakage. Gaussian noise augmentation within training folds expands samples from 280 to 1,680. A three phase pipeline progresses from a baseline GCN with AAL (73.3% accuracy, AUC=0.74), to an optimised GCN with MSDL (84.0%, AUC=0.84), to a Graph Attention Network ensemble achieving 95.0% accuracy (AUC=0.98), outperforming all recent GNN-based benchmarks on ABIDE I. The 10.7-point gain from atlas substitution alone demonstrates that functional parcellation is the most impactful modelling decision. Gradient-based saliency and GNNExplainer analyses converge on the Posterior Cingulate Cortex and Precuneus as core Default Mode Network hubs, validating that model decisions reflect ASD neuropathology rather than acquisition artefacts. All code and datasets will be publicly released upon acceptance.

Syeda Hareem Madani, Noureen Bibi, Adam Rafiq Jeraj +3

Salim Habib University · VentureDive · National University of Computer and Emerging Sciences

Autism Spectrum DisorderNeuroimagingExplainable Medical Image AnalysisGraph Attention NetworksFunctional Connectivity
AskPDF
Mar 2, 2026

Retrieving Patient-Specific Radiomic Feature Sets for Transparent Knee MRI Assessment

Classical radiomic features are designed to quantify image appearance and intensity patterns. Compared with end-to-end deep learning (DL) models trained for disease classification, radiomics pipelines with low-dimensional parametric classifiers offer enhanced transparency and interpretability, yet often underperform because of the reliance on population-level predefined feature sets. Recent work on adaptive radiomics uses DL to predict feature weights over a radiomic pool, then thresholds these weights to retain the top-k features from large radiomic pool F (often ~10^3). However, such marginal ranking can over-admit redundant descriptors and overlook complementary feature interactions. We propose a patient-specific feature-set selection framework that predicts a single compact feature set per subject, targeting complementary and diverse evidence rather than marginal top-k features. To overcome the intractable combinatorial search space of F choose k features, our method utilizes a 2-stage retrieval strategy: randomly sample diverse candidate feature sets, then rank these sets with a learned scoring function to select a high-performing feature set for the specific patient. The system consists of a feature-set scorer, and a classifier that performs the final diagnosis. We empirically show that the proposed two-stage retrieval approximates the original exhaustive all k-feature selection. Validating on tasks including ACL tear detection and KL grading for osteoarthritis, the experimental results achieve diagnostic performance, outperforming the top-k approach with the same k values, and competitive with end-to-end DL models while maintaining high transparency. The model generates auditable feature sets that link clinical outcomes to specific anatomical regions and radiomic families, allowing clinicians to inspect which anatomical structures and quantitative descriptors drive the prediction.

Yaxi Chen, Simin Ni, Jingjing Zhang +7

Mechanical Engineering Department, University College London, London, UK · Hawkes Institute, University College London, London, UK · Institute of Orthopaedic & Musculoskeletal Science, University College London, Royal National Orthopaedic Hospital, Stanmore, UK +4

RadiomicsExplainable Medical Image AnalysisMedical Image ClassificationMagnetic Resonance Imaging
AskPDF
Feb 28, 2026

Explainable Continuous-Time Mask Refinement with Local Self-Similarity Priors for Medical Image Segmentation

Accurate semantic segmentation of foot ulcers is essential for automated wound monitoring, yet boundary delineation remains challenging due to tissue heterogeneity and poor contrast with surrounding skin. To overcome the limitations of standard intensity-based networks, we present LSS-LTCNet:an ante-hoc explainable framework synergizing deterministic structural priors with continuous-time neural dynamics. Our architecture departs from traditional black-box models by employing a Local Self-Similarity (LSS) mechanism that extracts dense, illumination-invariant texture descriptors to explicitly disentangle necrotic tissue from background artifacts. To enforce topological precision, we introduce a Liquid Time-Constant (LTC) refinement module that treats boundary evolution as an ODEgoverned dynamic system, iteratively refining masks over continuous time-steps. Comprehensive evaluation on the MICCAI FUSeg dataset demonstrates that LSS-LTCNet achieves state-of-the-art boundary alignment, securing a peak Dice score of 86.96% and an exceptional 95th percentile Hausdorff Distance (HD95) of 8.91 pixels. Requiring merely 25.70M parameters, the model significantly outperforms heavier U-Net and transformer baselines in efficiency. By providing inherent visual audit trails alongside high-fidelity predictions, LSS-LTCNet offers a robust and transparent solution for computer-aided diagnosis in mobile healthcare (mHealth) settings.

Rajdeep Chatterjee, Sudip Chakrabarty, Trishaani Acharjee

School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India · AmygdalaAI-India Lab, Bhubaneswar, India

Medical Image SegmentationBoundary-Aware SegmentationExplainable Medical Image Analysis
AskPDF
Feb 28, 2026

An Interpretable Local Editing Model for Counterfactual Medical Image Generation

Counterfactual medical image generation have emerged as a critical tool for enhancing AI-driven systems in medical domain by answering "what-if" questions. However, existing approaches face two fundamental limitations: First, they fail to prevent unintended modifications, resulting collateral changes in demographic attributes when only disease features should be affected. Second, they lack interpretability in their editing process, which significantly limits their utility in real-world medical applications. To address these limitations, we present InstructX2X, a novel interpretable local editing model for counterfactual medical image generation featuring Region-Specific Editing. This approach restricts modifications to specific regions, effectively preventing unintended changes while simultaneously providing a Guidance Map that offers inherently interpretable visual explanations of the editing process. Additionally, we introduce MIMIC-EDIT-INSTRUCTION, a dataset for counterfactual medical image generation derived from expert-verified medical VQA pairs. Through extensive experiments, InstructX2X achieve state-of-the-art performance across all major evaluation metrics. Our model successfully generates high-quality counterfactual chest X-ray images along with interpretable explanations.

Hyungi Min, Taeseung You, Hangyeul Lee +2

Image EditingMedical Image SynthesisControllable Image GenerationExplainable Medical Image Analysis
AskPDF
Feb 27, 2026

Unsupervised Causal Prototypical Networks for De-biased Interpretable Dermoscopy Diagnosis

Despite the success of deep learning in dermoscopy image analysis, its inherent black-box nature hinders clinical trust, motivating the use of prototypical networks for case-based visual transparency. However, inevitable selection bias in clinical data often drives these models toward shortcut learning, where environmental confounders are erroneously encoded as predictive prototypes, generating spurious visual evidence that misleads medical decision-making. To mitigate these confounding effects, we propose CausalProto, an Unsupervised Causal Prototypical Network that fundamentally purifies the visual evidence chain. Framed within a Structural Causal Model, we employ an Information Bottleneck-constrained encoder to enforce strict unsupervised orthogonal disentanglement between pathological features and environmental confounders. By mapping these decoupled representations into independent prototypical spaces, we leverage the learned spurious dictionary to perform backdoor adjustment via do-calculus, transforming complex causal interventions into efficient expectation pooling to marginalize environmental noise. Extensive experiments on multiple dermoscopy datasets demonstrate that CausalProto achieves superior diagnostic performance and consistently outperforms standard black box models, while simultaneously providing transparent and high purity visual interpretability without suffering from the traditional accuracy compromise.

Junhao Jia, Yueyi Wu, Huangwei Chen +4

Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science and Technology, Zhejiang University · Hangzhou Dianzi University · Xi’an Jiaotong University

Explainable Medical Image AnalysisSkin Lesion ClassificationDebiased MLPrototype LearningPrototype-Based ClassificationCausal Representation Learning
AskPDF
Feb 26, 2026

GazeXPErT: An Expert Eye-tracking Dataset for Interpretable and Explainable AI in Oncologic FDG-PET/CT Scans

[18F]FDG-PET/CT is a cornerstone imaging modality for guiding oncology therapies, yet human expert shortages necessitate more efficient diagnostic aids. While standalone AI models for automatic lesion detection exist, clinical translation remains hindered by AI explainability, reliability, and workflow integration. Meanwhile, human-computer-interaction in radiology remain limited to keyboard, mouse and voice, ignoring experts' faster, natural gaze signal. We present GazeXPErT, a 4D eye-tracking dataset with annotated expert decision windows for tumor detection and measurement on 346 dual-read FDG-PET/CTs. The dataset contributes 9,030 gaze-to-lesion trajectories derived from 3,948 minutes of 60 Hz eye-tracking data, rendered in COCO-style format. GazeXPErT captures experts' visual reasoning patterns when adjudicating suspicious lesions. It aims to facilitate development of trusted, explainable and interactive AI models through understanding expert gaze patterns. Baseline feasibility experiments suggest salient signal is extractable from routinely collected expert gaze (3D nnU-Net Dice: 0.6819 versus 0.6008 without), that gaze-trained vision transformers may aid dynamic lesion localization (74.95% predicted gaze closer to tumor), and that experts' intent may be predictable from raw gaze (Accuracy 67.53%, AUROC 0.747).

Joy T Wu, Daniel Beckmann, Sarah Miller +15

Stanford University/Stanford Health Care, Radiology Department, Stanford, CA 94305, USA · University of Münster, Computer Vision and Machine Learning Systems, Heisenbergstraße 2, Münster 48149, Germany · University of Illinois Urbana-Champaign, School of Information Sciences, Champaign, IL 61820, USA +1

Explainable Medical Image AnalysisMedical Image BenchmarksMedical Image LocalizationPET/CT Imaging
AskPDF
Feb 26, 2026

SFL-Net: Source-Factorized Latent Representation Learning for Multi-Contrast MRI to Tau-PET Synthesis

Tau positron emission tomography supports Alzheimer's disease staging but is difficult to scale because of tracer, scanner, and radiation constraints. Synthesis from structural MRI is therefore attractive, but it is a particularly difficult setting. T1-weighted and FLAIR MRI provide anatomy and disease correlated morphology, but they do not directly measure Tau-PET relevant signal. We introduce SFL-Net, a multi-input synthesis framework that predicts Tau-PET from T1-weighted and FLAIR MRI. SFL-Net factorizes the latent representation into shared, T1-specific, FLAIR-specific, and complementary pathways and preserves anatomical detail through latent structural conditioning rather than direct encoder-decoder connections. We evaluated SFL-Net and baseline models using 605 training and 83 validation subjects from ADNI-3 and OASIS-3 datasets. Evaluation included raw image fidelity, standardized uptake value ratio agreement, high uptake overlap, regional Bland-Altman bias, braak derived stage agreement, non-inferiority sensitivity analysis, and latent component Shapley attribution. SFL-Net performed competitively on both clinically relevant and reconstruction metrics, while also delivering explicit source level auditability that conventional UNet derived models lack.

Agamdeep S. Chopra, Caitlin Neher, Tianyi Ren +3

Department of Mechanical Engineering, University of Washington, Seattle, WA, USA · Department of Radiology, University of Washington, Seattle, WA, USA

Disentangled Representation LearningMultimodal Disentangled Representation LearningMagnetic Resonance ImagingMedical Image SynthesisExplainable Medical Image Analysis
AskPDF
Feb 25, 2026

Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis

Computer-aided diagnosis (CAD) has significantly advanced automated chest X-ray diagnosis but remains isolated from clinical workflows and lacks reliable decision support and interpretability. Human-AI collaboration seeks to enhance the reliability of diagnostic models by integrating the behaviors of controllable radiologists. However, the absence of interactive tools seamlessly embedded within diagnostic routines impedes collaboration, while the semantic gap between radiologists' decision-making patterns and model representations further limits clinical adoption. To overcome these limitations, we propose a visual cognition-guided collaborative network (VCC-Net) to achieve the cooperative diagnostic paradigm. VCC-Net centers on visual cognition (VC) and employs clinically compatible interfaces, such as eye-tracking or the mouse, to capture radiologists' visual search traces and attention patterns during diagnosis. VCC-Net employs VC as a spatial cognition guide, learning hierarchical visual search strategies to localize diagnostically key regions. A cognition-graph co-editing module subsequently integrates radiologist VC with model inference to construct a disease-aware graph. The module captures dependencies among anatomical regions and aligns model representations with VC-driven features, mitigating radiologist bias and facilitating complementary, transparent decision-making. Experiments on the public datasets SIIM-ACR, EGD-CXR, and self-constructed TB-Mouse dataset achieved classification accuracies of 88.40%, 85.05%, and 92.41%, respectively. The attention maps produced by VCC-Net exhibit strong concordance with radiologists' gaze distributions, demonstrating a mutual reinforcement of radiologist and model inference. The code is available at https://github.com/IPMI-NWU/VCC-Net.

Shaoxuan Wu, Jingkun Chen, Chong Ma +3

College of Computer Science, Northwest University, Xi’an, China, 710127 · Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom · School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China +1

Medical Image AnalysisChest X-Ray ClassificationHuman-AI CollaborationExplainable Medical Image AnalysisMedical Image Localization
AskPDF
Feb 24, 2026

XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence

Deep learning has significantly advanced automated brain tumor diagnosis, yet clinical adoption remains limited by interpretability and computational constraints. Conventional models often act as opaque ''black boxes'' and fail to quantify the complex, irregular tumor boundaries that characterize malignant growth. To address these challenges, we present XMorph, an explainable and computationally efficient framework for fine-grained classification of three prominent brain tumor types: glioma, meningioma, and pituitary tumors. We propose an Information-Weighted Boundary Normalization (IWBN) mechanism that emphasizes diagnostically relevant boundary regions alongside nonlinear chaotic and clinically validated features, enabling a richer morphological representation of tumor growth. A dual-channel explainable AI module combines GradCAM++ visual cues with LLM-generated textual rationales, translating model reasoning into clinically interpretable insights. The proposed framework achieves a classification accuracy of 96.0%, demonstrating that explainability and high performance can co-exist in AI-based medical imaging systems. The source code and materials for XMorph are all publicly available at: https://github.com/ALSER-Lab/XMorph.

Sepehr Salem Ghahfarokhi, M. Moein Esfahani, Raj Sunderraman +2

Department of Computer Science, Georgia State University, Atlanta, GA, USA · TReNDS Center, Georgia State University, Atlanta, GA, USA

Brain Tumor ClassificationExplainable Medical Image AnalysisExplainable Image Classification
AskPDF
Feb 24, 2026

Leveraging Causal Reasoning Method for Explaining Medical Image Segmentation Models

Medical image segmentation plays a vital role in clinical decision-making, enabling precise localization of lesions and guiding interventions. Despite significant advances in segmentation accuracy, the black-box nature of most deep models has raised growing concerns about their trustworthiness in high-stakes medical scenarios. Current explanation techniques have primarily focused on classification tasks, leaving the segmentation domain relatively underexplored. We introduced an explanation model for segmentation task which employs the causal inference framework and backpropagates the average treatment effect (ATE) into a quantification metric to determine the influence of input regions, as well as network components, on target segmentation areas. Through comparison with recent segmentation explainability techniques on two representative medical imaging datasets, we demonstrated that our approach provides more faithful explanations than existing approaches. Furthermore, we carried out a systematic causal analysis of multiple foundational segmentation models using our method, which reveals significant heterogeneity in perceptual strategies across different models, and even between different inputs for the same model. Suggesting the potential of our method to provide notable insights for optimizing segmentation models. Our code can be found at https://github.com/lcmmai/PdCR.

Limai Jiang, Ruitao Xie, Bokai Yang +6

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Shenzhen University of Advanced Technology +3

Explainable Medical Image AnalysisMedical Image SegmentationCausal Interpretability
AskPDF
Feb 22, 2026

RetinaVision: XAI-Driven Augmented Regulation for Precise Retinal Disease Classification using deep learning framework

Early and accurate classification of retinal diseases is critical to counter vision loss and for guiding clinical management of retinal diseases. In this study, we proposed a deep learning method for retinal disease classification utilizing optical coherence tomography (OCT) images from the Retinal OCT Image Classification - C8 dataset (comprising 24,000 labeled images spanning eight conditions). Images were resized to 224x224 px and tested on convolutional neural network (CNN) architectures: Xception and InceptionV3. Data augmentation techniques (CutMix, MixUp) were employed to enhance model generalization. Additionally, we applied GradCAM and LIME for interpretability evaluation. We implemented this in a real-world scenario via our web application named RetinaVision. This study found that Xception was the most accurate network (95.25%), followed closely by InceptionV3 (94.82%). These results suggest that deep learning methods allow effective OCT retinal disease classification and highlight the importance of implementing accuracy and interpretability for clinical applications.

Mohammad Tahmid Noor, Shayan Abrar, Jannatul Adan Mahi +3

Dept. of CSE East West University Dhaka, Bangladesh · Dept. of CSE American International University-Bangladesh Dhaka, Bangladesh · Dept. of ICT Bangladesh Army University of Engineering and Technology Dhaka, Bangladesh

Retinal Disease ClassificationMedical Image ClassificationImage ClassificationExplainable Medical Image Analysis
AskPDF
Feb 22, 2026

EMAD: Evidence-Centric Grounded Multimodal Diagnosis for Alzheimer's Disease

Deep learning models for medical image analysis often act as black boxes, seldom aligning with clinical guidelines or explicitly linking decisions to supporting evidence. This is especially critical in Alzheimer's disease (AD), where predictions should be grounded in both anatomical and clinical findings. We present EMAD, a vision-language framework that generates structured AD diagnostic reports in which each claim is explicitly grounded in multimodal evidence. EMAD uses a hierarchical Sentence-Evidence-Anatomy (SEA) grounding mechanism: (i) sentence-to-evidence grounding links generated sentences to clinical evidence phrases, and (ii) evidence-to-anatomy grounding localizes corresponding structures on 3D brain MRI. To reduce dense annotation requirements, we propose GTX-Distill, which transfers grounding behavior from a teacher trained with limited supervision to a student operating on model-generated reports. We further introduce Executable-Rule GRPO, a reinforcement fine-tuning scheme with verifiable rewards that enforces clinical consistency, protocol adherence, and reasoning-diagnosis coherence. On the AD-MultiSense dataset, EMAD achieves state-of-the-art diagnostic accuracy and produces more transparent, anatomically faithful reports than existing methods. We will release code and grounding annotations to support future research in trustworthy medical vision-language models.

Qiuhui Chen, Xuancheng Yao, Zhenglei Zhou +2

East China University of Science and Technology · Shanghai Jiao Tong University · Tencent

Medical VLMsExplainable Medical Image AnalysisMedical Report GenerationMedical Image GroundingAlzheimer's Disease Classification
AskPDF
Feb 21, 2026

Similarity-as-Evidence: Calibrating Overconfident VLMs for Interpretable and Label-Efficient Medical Active Learning

Active Learning (AL) reduces annotation costs in medical imaging by selecting only the most informative samples for labeling, but suffers from cold-start when labeled data are scarce. Vision-Language Models (VLMs) address the cold-start problem via zero-shot predictions, yet their temperature-scaled softmax outputs treat text-image similarities as deterministic scores while ignoring inherent uncertainty, leading to overconfidence. This overconfidence misleads sample selection, wasting annotation budgets on uninformative cases. To overcome these limitations, the Similarity-as-Evidence (SaE) framework calibrates text-image similarities by introducing a Similarity Evidence Head (SEH), which reinterprets the similarity vector as evidence and parameterizes a Dirichlet distribution over labels. In contrast to a standard softmax that enforces confident predictions even under weak signals, the Dirichlet formulation explicitly quantifies lack of evidence (vacuity) and conflicting evidence (dissonance), thereby mitigating overconfidence caused by rigid softmax normalization. Building on this, SaE employs a dual-factor acquisition strategy: high-vacuity samples (e.g., rare diseases) are prioritized in early rounds to ensure coverage, while high-dissonance samples (e.g., ambiguous diagnoses) are prioritized later to refine boundaries, providing clinically interpretable selection rationales. Experiments on ten public medical imaging datasets with a 20% label budget show that SaE attains state-of-the-art macro-averaged accuracy of 82.57%. On the representative BTMRI dataset, SaE also achieves superior calibration, with a negative log-likelihood (NLL) of 0.425.

Zhuofan Xie, Zishan Lin, Jinliang Lin +3

School of Electronic Technology and Engineering, Xiamen University, Xiamen, China · School of Informatics, Xiamen University, Xiamen, China · School of Engineering, Case Western Reserve University, Cleveland, USA

Active LearningVLM CalibrationMedical VLMsMedical Image ClassificationEvidential Deep LearningExplainable Medical Image Analysis
AskPDF
Feb 20, 2026

RamanSeg: Interpretability-driven Deep Learning on Raman Spectra for Cancer Diagnosis

Histopathology, the current gold standard for cancer diagnosis, involves the manual examination of tissue samples after chemical staining, a time-consuming process requiring expert analysis. Raman spectroscopy is an alternative, stain-free method of extracting information from samples. Using nnU-Net, we trained a segmentation model on a novel dataset of spatial Raman spectra aligned with tumour annotations, achieving a mean foreground Dice score of 80.9%, surpassing previous work. Furthermore, we propose a novel, interpretable, prototype-based architecture called RamanSeg. RamanSeg classifies pixels based on discovered regions of the training set, generating a segmentation mask. Two variants of RamanSeg allow a trade-off between interpretability and performance: one with prototype projection and another projection-free version. The projection-free RamanSeg outperformed a U-Net baseline with a mean foreground Dice score of 67.3%, offering a meaningful improvement over a black-box training approach.

Chris Tomy, Mo Vali, David Pertzborn +9

Department of Computer Science and Technology, University of Cambridge, Cambridge, UK · Department of Physics, University of Cambridge, Cambridge, UK · Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany +2

Image SegmentationMedical DiagnosisExplainable Medical Image AnalysisPrototype LearningRaman Spectroscopy
AskPDF
Feb 17, 2026

BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features

Recent advances in radiology report generation (RRG) have been driven by large paired image-text datasets; however, progress in neuro-oncology has been limited due to a lack of open paired image-report datasets. Here, we introduce BTReport, an open-source framework for brain tumor RRG that constructs natural language radiology reports using deterministically extracted imaging features. Unlike existing approaches that rely on large general-purpose or fine-tuned vision-language models for both image interpretation and report composition, BTReport performs deterministic feature extraction for image analysis and uses large language models only for syntactic structuring and narrative formatting. By separating RRG into a deterministic feature extraction step and a report generation step, the generated reports are completely interpretable and less prone to hallucinations. We show that the features used for report generation are predictive of key clinical outcomes, including survival and IDH mutation status, and reports generated by BTReport are more closely aligned with reference clinical reports than existing baselines for RRG. Finally, we introduce BTReport-BraTS, a companion dataset that augments BraTS imaging with synthetically generated radiology reports produced with BTReport. Code for this project can be found at https://github.com/KurtLabUW/BTReport.

Juampablo E. Heras Rivera, Dickson T. Chen, Tianyi Ren +4

University of Washington · University of Washington School of Medicine · Microsoft Health AI +1

Radiology Report GenerationText GenerationExplainable Medical Image Analysis
AskPDF
Feb 16, 2026

MacNet: An End-to-End Manifold-Constrained Adaptive Clustering Network for Interpretable Whole Slide Image Classification

Whole slide images (WSIs) are the gold standard for pathological diagnosis and sub-typing. Current main-stream two-step frameworks employ offline feature encoders trained without domain-specific knowledge. Among them, attention-based multiple instance learning (MIL) methods are outcome-oriented and offer limited interpretability. Clustering-based approaches can provide explainable decision-making process but suffer from high dimension features and semantically ambiguous centroids. To this end, we propose an end-to-end MIL framework that integrates Grassmann re-embedding and manifold adaptive clustering, where the manifold geometric structure facilitates robust clustering results. Furthermore, we design a prior knowledge guiding proxy instance labeling and aggregation strategy to approximate patch labels and focus on pathologically relevant tumor regions. Experiments on multicentre WSI datasets demonstrate that: 1) our cluster-incorporated model achieves superior performance in both grading accuracy and interpretability; 2) end-to-end learning refines better feature representations and it requires acceptable computation resources.

Mingrui Ma, Chentao Li, Pan Huang +1

School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China · Affiliated Tumor Hospital of Xinjiang Medical University, Xinjiang 830011, China · Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY 10027, USA +1

Histopathology Image ClassificationExplainable Medical Image AnalysisMultiple Instance LearningClustering
AskPDF