Explainable Medical Image Analysis

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

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RadiomicNet: A Hybrid Radiomics-Guided Lightweight Architecture for Interpretable Medical Image Segmentation
Jul 2, 2026Mohammad Amanour RahmanUltrasound Image Segmentation Medical Image Segmentation
Department of Computer Science and Engineering Ahsanullah University of Science and Technology (AUST) Dhaka, Bangladesh
Boosting Ultrasound Image Classification via Attribute-Guided Dual-Branch Framework
Jul 2, 2026Bo Zhao, Yapeng Li, Juhua Liu +1Medical Image Classification Ultrasound Imaging
School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China
Beyond Heatmaps: Unsupervised Concept-Graph Reasoning for Interpretable Visual Explanation
Jul 1, 2026Md Mohasin Hossain, Anar Amirli, Robert Leist +2Concept Bottleneck Models Concept-Based Explanations
German Research Center for Artificial Intelligence · Saarland University, Saarbrücken, Germany · 1German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany +5
MalariAI: A Label-Resilient Decoupled Framework for Universal Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears
Jul 1, 2026Kaysarul Anas Apurba, Md Hasibul Hasan, Mohammed Ali +1Explainable Medical Image Analysis Microscopy Image Analysis
Department of Electrical and Computer Engineering, North South University, Dhaka 1229, Bangladesh · Department of Computer Science and Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka 1230, Bangladesh
GRAPE: Graph-Augmented Prototype Explanations for Interactive Medical Image Diagnosis
Jun 29, 2026Rasul Khanbayov, Erchin Serpedin, Hasan KurbanPrototype-Based Classification Medical Image Classification
1Hamad Bin Khalifa University · 2Texas A&M University
TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment
Jun 29, 2026Alia Tarek, Hamsa Saberr, Hamza Elghonemy +6Concept Bottleneck Models 3D Medical Imaging
Department of Biomedical and Healthcare Data Engineering, Faculty of Engineering, Cairo University, Giza, Egypt · German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany · University of Oldenburg, Oldenburg, Germany
DrivenMorph: Bridging Attention Mechanism and Variational Image Registration via Difference Modeling
Jun 29, 2026Mingke Li, Jianping Zhang, Jinqiu Deng3D Medical Imaging Explainable Medical Image Analysis
School of Mathematics and Computational Science, Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, Hunan 411105, P. R. China
Latent-CURE for Breast Cancer Diagnosis
Jun 29, 2026Weiyi Zhao, Xiaoyu Tan, Lu Gan +2Breast Cancer Long-Tail Learning
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China · Tencent YouTu Lab, Shanghai 200232, China · Integrative Clinical Research Ward, Clinical Medicine Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
Interpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features
Jun 26, 2026Aixa X. AndradeClinical Outcome Prediction Explainable Medical Image Analysis
Lyda Hill Department of Bioinformatics · Department of Biomedical Engineering · University of Texas Southwestern Medical Center, Dallas, Texas, USA
EchoSonar-R: A Multi-View Reasoning-Enabled Model for Disease Classification and Report Generation in Echocardiography
Jun 26, 2026Darya Taratynova, Ahmed Aly, Numan Saeed +1Medical Report Generation Echocardiography
Division of Computing and Mathematical Sciences, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE
Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
Jun 25, 2026Santanu Ganguly, Xing Liang, Dimitrios MakrisMedical Image Anomaly Detection Quantum Machine Learning
Kingston University, Kingston upon Thames KT1 2EE, UK Quantum AI (QAI) Research Group School of Computer Science and Mathematics (CSM) Faculty of Engineering, Computing and the Environment (ECE)
Revealing Mammographic Phenotypes in Deep Learning Breast Cancer Risk Models
Jun 24, 2026Ruiyu Jia, Yanqi Xu, Yuxuan Chen +2Breast Cancer Medical Image Analysis
Department of Mathematics, New York University Shanghai, China · Center for Data Science, New York University, USA · Perlmutter Cancer Center, NYU Langone Health, New York, USA +1
FunPiQ: A New Benchmark for Pixel-Level Quality Assessment in Fundus Images
Jun 24, 2026Pengwei Wang, José Morano, Virginia Mares +1Fundus Imaging Explainable Medical Image Analysis
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria · Christian Doppler Lab for Artificial Intelligence in Retina, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
Blasto-Net: An Explainable Multi-Task Learning for Blastocyst Segmentation, Grading, and Implantation Prediction
Jun 24, 2026Zahra Asghari Varzaneh, Reza Khoshkangini, Magnus Johnsson +2Clinical Outcome Prediction Multi-Task Learning
Department of Computer Science and Media Technology, Malmö University, Malmö, Sweden · Research Environment of Computer Science, Kristianstad University, Sweden · Kepler University Hospital, Krankenhausstr, Linz, Austria +1
Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations
Jun 23, 2026Wistan Marchadour, Pedro Soto Vega, Franck Vermet +1Explainable Medical Image Analysis Explainability Evaluation
LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France · LMBA, CNRS, UMR 6205, Univ Brest, Brest, France
Jolia: Concept-Level Vision-Language Alignment for 3D CT Contrastive Learning
Jun 23, 2026Julien Khlaut, Charles Corbière, Baptiste Callard +93D Foundation Models Medical Imaging Foundation Models
Raidium, Paris, 75014, France · Department of Vascular and Oncological Interventional Radiology,2026 Hôpital Européen Georges Pompidou, AP-HP, Paris, France · Faculté de Santé, Université Paris-Cité, Paris, France +3
A Dual Edge Spatial Jacobian Image Graph for Interpretable Diabetic Retinopathy Grading
Jun 23, 2026Inam Ullah, Imran Razzak, Shoaib JameelGraph Representation Learning Diabetic Retinopathy Grading
University of Southampton Southampton, United Kingdom · Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, United Arab Emirates
Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability
Jun 22, 2026Qi Li, Yuliang Huang, Shaheer U. Saeed +7Gaussian Processes Medical Image Segmentation
UCL Hawkes Institute, Department of Medical Physics and Biomedical Engineering, University College London, London, U.K. · Centre for Bioengineering, Digital Environment Research Institute, School of Engineering and Materials Science, Queen Mary University of London, London, U.K. · Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, U.K. +4
IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection
Jun 22, 2026Haibiao Li, Di Lin, Xue Jiang +3Skin Lesion Classification Vision Transformer
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, P. R. China · Department of Dermatology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, 400021, P. R. China · Chongqing Health Center for Women and Children, Chongqing, 400013, P. R. China
EnTrust: Modeling Inter-Modal Conflict for Trustworthy Multimodal Medical Image Analysis
Jun 19, 2026Dwarikanath Mahapatra, Abhijit Das, Behzad Bozorgtabar +5Cross-Modality Medical Image Segmentation Uncertainty-Aware Multimodal Fusion
Khalifa University, Abu Dhabi, UAE. · MBZUAI, Abu Dhabi, UAE. · Aarhus University, Denmark. +5
Graph-of-Differences: Anatomy-Structured Difference Alignment for Medical Image Re-Identification
Jun 19, 2026Nichula Wasalathilaka, Abhijit Das, Imran Razzak +1Image Matching Medical Image Analysis
Khalifa University, Abu Dhabi, UAE. · MBZUAI, Abu Dhabi, UAE. · MedOS, Abu Dhabi, UAE. +1
MammoExpert: Benchmarking Chain-of-Thought Reasoning in Mammography Diagnosis
Jun 19, 2026Di Dai, Bo Liu, Youcheng Li +9Medical Imaging CoT Reasoning
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University Beijing, China · School of Computer Science and Engineer, Beijing University of Aeronautics and Astronautics Beijing, China · Center for Data Science, Peking University Beijing, China +5
Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning
Jun 18, 2026Zahra Asghari Varzaneh, Reza Khoshkangini, Thomas Ebner +1Explainable Medical Image Analysis Microscopy Image Analysis
Department of Computer Science and Media Technology, Malmö University, Malmö, Sweden · Kepler Universitätsklinikum, Linz, Austria · NewLifeAid-Global AB, Sweden
EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors
Jun 18, 2026Pengwei Wang, José Morano, Qian Wan +1Fundus Imaging Explainable Medical Image Analysis
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria · Christian Doppler Lab for Artificial Intelligence in Retina, Medical University of Vienna, Austria
HypOProto: Hyperbolic Ordinal Prototypes for Left Ventricular Filling Pressure Classification
Jun 18, 2026Victoria Wu, Nima Hashemi, Hooman Vaseli +3Prototype-Based Classification Echocardiography
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada · Vancouver General Hospital, Vancouver, BC, Canada
GUMP-Net: An interpretable model-data-driven intelligent algorithm for multi-class pelvic segmentation
Jun 17, 2026Liheng Wang, Yinghui Zhang, Licheng Zhang +3Medical Image Segmentation Explainable Medical Image Analysis
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China. · University of Chinese Academy of Sciences, Beijing 100190, China. · State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. +4
BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection
Jun 17, 2026Md Taimur Ahad, Bo Song, Yan LiHybrid CNN-Transformer Architectures Explainable Medical Image Analysis
School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia · School of Engineering, University of Southern Queensland, Toowoomba, QLD 4350, Australia
When LLMs Analyze Scars: From Images to Clinically-Meaningful Features
Jun 16, 2026Ruman Wang, Hangting YeMedical Diagnosis Medical Image Classification
Liaoning University of Traditional Chinese Medicine · School of Artificial Intelligence, Jilin University
LLM-Based Visual Explanation Evaluation Framework for Assessing the Explainability of Facial Skin Disease Classification Models
Jun 15, 2026Gyuyeon NaSkin Lesion Classification LLM-as-a-Judge
AI and Business Analytics, Ewha Womans University, Seoul, Republic of Korea
Input-Dependent Fisher Information for Local Sensitivity Analysis of Medical Image Classifiers
Jun 15, 2026Sourya Sengupta. Mark A. AnastasioNeural Network Interpretability Medical Image Classification
Department of Electrical and Computer Engineering, University of Illinois Urbana–Champaign, Urbana, IL 61801 USA

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RadiomicNet: A Hybrid Radiomics-Guided Lightweight Architecture for Interpretable Medical Image Segmentation

Mohammad Amanour Rahman

Department of Computer Science and Engineering Ahsanullah University of Science and Technology (AUST) Dhaka, Bangladesh

Ultrasound Image Segmentation Medical Image Segmentation Explainable Medical Image Analysis Radiomics

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Deep learning has achieved remarkable performance in medical image segmentation, yet it suffers from critical limitations: mathematical intractability, substantial parameter requirements, and lack of clinical interpretability. We propose RadiomicNet, a novel two-stream hybrid architecture that enhances standard deep learning by integrating handcrafted radiomics features directly into the segmentation learning process. The key contribution is the Radiomics Attention Gate (RAG), which leverages Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) features to modulate skip-connection attention in a lightweight MobileNetV2-based encoder-decoder, providing ante-hoc interpretability without post-hoc approximations. A novel Radiomics Consistency Loss further enforces alignment between texture complexity and prediction uncertainty, reducing Expected Calibration Error (ECE) from 0.142 to 0.118. RadiomicNet achieves a Dice Similarity Coefficient (DSC) of 0.763 +/- 0.231 on the Breast Ultrasound Images (BUSI) dataset and 0.854 +/- 0.112 on Kvasir-SEG, outperforming U-KAN by 1.2% and 1.8%, respectively (p < 0.05, Wilcoxon signed-rank test), with only 3.27M parameters, 9.5x fewer than standard U-Net and 4.3x fewer than U-KAN. Gradient-based feature importance analysis reveals that GLCM dissimilarity (15.24%), GLCM energy (14.56%), and LBP entropy (11.49%) are the dominant radiomics cues, providing clinically meaningful explanations for segmentation decisions. The proposed approach demonstrates that compact, interpretable models grounded in domain knowledge can deliver state-of-the-art segmentation performance with substantially reduced computational overhead.

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