Explainable Medical Image Analysis

Follow this topic Ask about this topic

Momentum

12 papers in the last four weeks, up 33% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 156

All topics

Trusting Right Predictions for Wrong Reasons: A LIME Based Analysis of Deep Learning Interpretability in Lung Cancer Diagnosis
Jun 14, 2026Samarpan Poudel, Vladislav D VekslerExplainable Artificial Intelligence Explanation Stability
Caldwell University School of Business and Computer Science Caldwell, NJ 07006
NeRD: Neuro-Symbolic Rule Distillation for Efficient Ontology-Grounded Chain-of-Thought in Medical Image Diagnosis
Jun 14, 2026Hongxi Yang, Yiwen Jiang, Siyuan Yan +8Medical Image Classification Multimodal CoT Reasoning
Department of Data Science & AI, Faculty of Information Technology, Monash University, Melbourne, Australia · AIM for Health Lab, Faculty of Information Technology, Monash University, Melbourne, Australia · Faculty of Engineering, Monash University, Melbourne, Australia +2
HSQ-VLM: A Novel Spatially-Constrained Quadrant Segmentation VLM Model for Explainability in Diabetic Retinopathy
Jun 11, 2026Shivum TelangMedical VLMs Medical Image Segmentation
Pittsburgh, Pennsylvania
MAGIS: Evidence-Based Multi-Agent Reasoning for Interpretable Strabismus Clinical Decision-Making
Jun 8, 2026Xikai Tang, Yifan Wang, Jiafan Zhuang +12Medical Diagnosis Medical VLMs
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China · Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, 518000, China · Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou, 515041, China +5
Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology
Jun 4, 2026Yanqing Luo, Julius Hense, Niklas Prenißl +4Computational Pathology Explainable Medical Image Analysis
Berlin Institute for the Foundations of Learning and Data, Berlin, Germany · Machine Learning Group, Technische Universität Berlin, Berlin, Germany · Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany +10
An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)
Jun 3, 2026Jincheng Yu, Haoyang Li, Yiwen Liu +5Longitudinal Medical Image Analysis Clinical Outcome Prediction
Statistics & Data Science GIDP, University of Arizona · Department of Epidemiology and Biostatistics, University of Arizona · R. Kent Coit College of Pharmacy, University of Arizona +2
Attention Consistent Longitudinal Medical Visual Question Answering Guided by Vision Foundation Models
Jun 3, 2026Jialin Wu, Qianru Zhang, Georges El Fakhri +1Vision Foundation Model Adaptation Medical VQA
Yale Biomedical Imaging Institute New Haven, CT 06510
Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma
Jun 3, 2026Dilakshan Srikanthan, Amoon Jamzad, Paul Wilson +5Pathology Foundation Models Explainable Medical Image Analysis
Translational Medicine, School of Medicine, Queen’s University, Kingston, ON, Canada · School of Computing, Queen’s University, Kingston, ON, Canada · Department of Surgery, Queen’s University, Kingston, ON, Canada
A unified multi-task framework enables interpretable chest radiograph analysis
Jun 2, 2026Lijian Xu, Ziyu Ni, Xinglong Liu +3Radiology Report Generation Chest X-Ray Classification
Shenzhen University of Advanced Technology · the Chinese University of Hong Kong, Hong Kong · Lead contact +4
Learning Label-Efficient Interpretable Medical Image Diagnosis via Semi-supervised Hypergraph Concept Bottleneck Model
Jun 1, 2026Yijun Yang, Ruiqiang Xiao, Lijie Hu +4Concept Bottleneck Models Medical Image Classification
HKUST(GZ) · Joy Future Academy · MBZUAI +3
Counterfactual Explanations for Deep Two-Sample Testing
May 29, 2026Wei-Cheng Lai, Marco Simnacher, Christoph LippertTwo-Sample Testing Counterfactual Explanations
Digital Health and Machine Learning Hasso-Plattner-Institute, University of Potsdam Potsdam, Germany · Chair of Statistics Humboldt-Universität zu Berlin Berlin, Germany · Hasso Plattner Institute for Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai New York, United States of America
VITAL: Visual-Semantic Dual Supervision for Enhanced and Interpretable Latent Reasoning in Medical MLLMs
May 27, 2026Qiaoru Li, Shaotian Liang, Jintao Chen +4Medical VQA VLM Interpretability
3Tencent · 1Zhejiang University · 2Shanghai AI Laboratory +1
Clinical Validation of the Melanoscope AI Mobile Dermoscopy Clinical Decision Support System
May 26, 2026Elena Sergeevna Kozachok, Sergey Sergeevich SereginSkin Lesion Classification Clinical Triage
1Ivannikov Institute for System Programming of the Russian Academy of Sciences (ISP RAS),2026 25 Alexander Solzhenitsyn str., Moscow, 109004, Russian Federation · 2Orel Regional Oncology Dispensary,May 2 Ippodromny lane, Orel, Russian Federation
Aligning Cellular Sheaves with Classifier Attention for Interpretable Weakly-Supervised Pathology Localization
May 24, 2026Devansh Lalwani, Swapnil Bhat, Maulik ShahExplainable Medical Image Analysis Medical Image Localization
Turocrates AI Private Limited, Mumbai, India
X-Edit: Exact, Explicit, and Explainable Null-Space Editing for Medical Vision Transformers
May 24, 2026Yuanye Liu, Siyuan Zhou, Ke Zhang +3Vision Transformer Medical Image Classification
Fudan University, Shanghai, China · Johns Hopkins University, Baltimore, USA · National University of Singapore, Singapore +1
Explainable Multi-Task Retinal Imaging Reveals Microvascular Signals for Systemic Risk Stratification in Type 2 Diabetes: A Pilot Study
May 24, 2026Mini Han Wang, Liting Huang, Wei Hong +1Medical Image Analysis Medical Image Classification
Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen, P.R.China · Frontier Science Computing Center, Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai, P.R.China · Chinese University of Hong Kong, Hong Kong China +2
Structured Visual Evidence Decomposition for Evidence-Grounded Multimodal Screening of Obstructive Sleep Apnea-Hypopnea Syndrome
May 23, 2026Chen Zhan, Yingchen Wei, Xiaoyu Tan +2Healthcare Clinical Triage
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 200438, China · Tencent Youtu Lab, Shanghai 200233, China · ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital of Fudan University, Shanghai 200031, China +1
SegGuidedNet: Sub-Region-Aware Attention Supervision for Interpretable Brain Tumor Segmentation
May 21, 2026Hasaan Maqsood, Saif Ur Rehman Khan, Sebastian Vollmer +2Image Segmentation BraTS
National University of Sciences and Technology (NUST), Islamabad, Pakistan. · Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany. · German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany. +2
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
ProtoPathway: Biologically Structured Prototype-Pathway Fusion for Multimodal Cancer Survival Prediction
May 20, 2026Amaya Gallagher-Syed, Costantino Pitzalis, Myles J. Lewis +2Cancer Survival Prediction Oncology
Queen Mary University of London
Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models
May 19, 2026Guangzhi Xiong, Qiao Jin, Sanchit Sinha +2Medical VQA VLM Interpretability
University of Virginia · National Institutes of Health
GCE-MIL: Faithful and Recoverable Evidence for Multiple Instance Learning in Whole-Slide Imaging
May 17, 2026Xiangyu Li, Ran SuExplainable Medical Image Analysis Histopathology Image Classification
College of Intelligence and Computing Tianjin University
Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability
May 17, 2026Nisreen Albzour, Sarah S. LamVision Transformer Efficient ViTs
School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
Diffusion Attention Expert Model for Predicting and Semi-automatic Localizing STAS in Lung Cancer Histopathological Images
May 15, 2026Liangrui Pan, Jiadi Luo, Yuxuan Xiao +13Computational Pathology Oncology
College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China · Department of Pathology, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China · Hunan Clinical Medical Research Center for Cancer Pathogenic Genes Testing and Diagnosis, Changsha, Hunan, 410011, China +8
Evidential Reasoning Advances Interpretable Real-World Disease Screening
May 14, 2026Chenyu Lian, Hong-Yu Zhou, Jing QinMedical Image Analysis Explainable Medical Image Analysis
The Center for Smart Health, School of Nursing, the Hong Kong Polytechnic University, Hong Kong, China · Research Institute for Smart Ageing, the Hong Kong Polytechnic University, Hong Kong, China · School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing, China
DermAgent: A Self-Reflective Agentic System for Dermatological Image Analysis with Multi-Tool Reasoning and Traceable Decision-Making
May 14, 2026Yize Liu, Siyuan Yan, Ming Hu +5Medical Image Analysis LLM Agent Orchestration
AIM for Health Lab, Faculty of Information Technology, Monash University, Melbourne, Australia · Faculty of Information Technology, Monash University, Melbourne, Australia · University College London, Institute of Ophthalmology, London, United Kingdom
Towards Fine-Grained and Verifiable Concept Bottleneck Models
May 14, 2026Yingying Fang, Haijie Xu, Shuang Wu +2Concept Bottleneck Models Neural Network Interpretability
Bioengineering Department and Imperial-X, Imperial College London, London, UK · Thoughtworks AI Labs, Singapore
ProtoMedAgent: Multimodal Clinical Interpretability via Privacy-Aware Agentic Workflows
May 13, 2026Alvaro Lopez Pellicer, Plamen Angelov, Marwan Bukhari +3LLM Hallucination Mitigation Healthcare
School of Computing and Communications, Lancaster University · Lancaster Medical School, Lancaster University · PUC-Rio, Puc-Behring Institute for AI
Robust and Explainable Bicuspid Aortic Valve Diagnosis Using Stacked Ensembles on Echocardiography
May 13, 2026Christos Chrysanthos Nikolaidis, Vasileios Sachpekidis, Nikolas Moustakidis +2Medical Diagnosis Echocardiography
Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, 67100, Greece · Department of Cardiology, Papageorgiou Hospital, Thessaloniki, Greece · Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece +1
Anatomy-Slot: Unsupervised Anatomical Factorization for Homologous Bilateral Reasoning in Retinal Diagnosis
May 13, 2026Yingzhe Ma, Xiao Yang, Yuguo Yin +1Medical Image Analysis Medical Image Classification
University of Electronic Science and Technology of China · Peking University

Jun 14, 2026, cs.CVJ/K move · Enter open · S save

Trusting Right Predictions for Wrong Reasons: A LIME Based Analysis of Deep Learning Interpretability in Lung Cancer Diagnosis

Samarpan Poudel, Vladislav D Veksler

Caldwell University School of Business and Computer Science Caldwell, NJ 07006

Explainable Artificial Intelligence Explanation Stability Medical Image Classification Explainable Medical Image Analysis Explainability Evaluation

Open paper AskPDF arXiv

Lung cancer is the leading cause of cancer-related mortality, with approximately 2.5 million new cases and 1.8 million deaths annually, making reliable diagnosis a clinical priority. Although deep learning models have achieved strong performance in lung cancer classification, evaluation has largely focused on predictive accuracy, leaving their decision-making processes insufficiently examined. This study compares three architecturally distinct models: a Convolutional Neural Network (CNN), a pretrained ResNet50, and a Vision Transformer (ViT), trained on the IQ-OTH/NCCD lung cancer CT dataset. Local Interpretable Model-Agnostic Explanations (LIME) were applied to investigate model reasoning. In addition to standard performance metrics, a dual-correlation framework was introduced to measure both prediction agreement and explanation agreement across model pairs. All three models achieved strong classification performance, with ResNet50 attaining 98.61% accuracy, CNN 97.91%, and ViT 93.75%, while all achieved ROC-AUC scores of 0.99. Prediction correlations exceeded 0.99 across all model pairs, indicating highly consistent outputs. However, LIME explanation correlations remained below 0.26, revealing substantial differences in the image regions used to reach those predictions. Analysis of misclassified samples further identified a consistent spatial pattern: incorrect predictions were associated with attention outside the lung parenchyma, whereas correct predictions focused primarily within lung regions. These findings demonstrate that prediction agreement is a poor proxy for reasoning consistency, and that interpretability evaluation must be treated as an independent validation criterion alongside predictive performance in clinical AI systems.

Momentum

Related topics

Latest papers 156