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

Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction
Oct 7, 2025Shuo Jiang, Zhuwen Chen, Liaoman Xu +6Computational Pathology Cancer Survival Prediction
Hangzhou Dianzi University, Hangzhou 310018, China · School of Information and Communication Technology, Griffith University, QLD 4215, Australia · Shenzhen Research Institute of Big Data, Shenzhen 518172, China +3
Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification
Jun 23, 2025Ciro Listone, Aniello MuranoVariational Autoencoders Skin Lesion Classification
University of Naples Federico II, Naples, Italy.
Explainability in mulimodal deep transformation models for stroke outcome prediction
Apr 7, 2025Lisa Herzog, Jonas Brändli, Maurice Schneeberger +7Clinical Outcome Prediction Explainable Medical Image Analysis
University Hospital Zurich, Department of Neurology, Frauenklinikstrasse 26, 8091 Zurich, Switzerland · Zurich University of Applied Sciences, Institute of Data Analysis and Process Design (IDP), Technikumstrasse 9, 8401 Winterthur, Switzerland · University of Zurich, Epidemiology, Biostatistics and Prevention Institute (EBPI), Hirschengraben 84, 8001 Zurich, Switzerland +1
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Feb 23, 2025Laurin Lux, Alexander H. Berger, Maria Romeo Tricas +8Medical Imaging Diabetic Retinopathy Grading
Munich Center for Machine Learning (MCML), Munich, Germany · School of Medicine and Health, TUM University Hospital, Munich, Germany · School of Computation, Information and Technology, TUM, Munich, Germany
Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration
Jul 18, 2024Abhijeet Parida, Antonia Alomar, Zhifan Jiang +7Image Enhancement Test-Time Adaptation
Children’s National Hospital, Washington, DC, USA · Universitat Pompeu Fabra, Barcelona, Spain · Universidad Polit´ecnica de Madrid, Madrid, Spain +2
Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images
May 12, 2024Fatema Tuj Johora Faria, Mukaffi Bin Moin, Pronay Debnath +2Image Segmentation Retinal Vessel Segmentation
aDepartment of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh

Oct 7, 2025, cs.CVJ/K move · Enter open · S save

Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction

Shuo Jiang, Zhuwen Chen, Liaoman Xu, Yanming Zhu+5

Hangzhou Dianzi University, Hangzhou 310018, China · School of Information and Communication Technology, Griffith University, QLD 4215, Australia · Shenzhen Research Institute of Big Data, Shenzhen 518172, China+3

Computational Pathology Cancer Survival Prediction Prototype Learning Multimodal Survival Analysis Explainable Medical Image Analysis Whole-Slide Image Analysis+1

Open paper AskPDF arXiv

Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which reduces their usefulness in clinical settings. Prototype learning presents a potential solution, yet traditional methods focus on local similarities and static matching, neglecting the broader tumor context and lacking strong semantic alignment with genomic data. To overcome these issues, we introduce an innovative prototype-based multimodal framework, FeatProto, aimed at enhancing cancer survival prediction by addressing significant limitations in current prototype learning methodologies within pathology. Our framework establishes a unified feature prototype space that integrates both global and local features of whole slide images (WSI) with genomic profiles. This integration facilitates traceable and interpretable decision-making processes. Our approach includes three main innovations: (1) A robust phenotype representation that merges critical patches with global context, harmonized with genomic data to minimize local bias. (2) An Exponential Prototype Update Strategy (EMA ProtoUp) that sustains stable cross-modal associations and employs a wandering mechanism to adapt prototypes flexibly to tumor heterogeneity. (3) A hierarchical prototype matching scheme designed to capture global centrality, local typicality, and cohort-level trends, thereby refining prototype inference. Comprehensive evaluations on four publicly available cancer datasets indicate that our method surpasses current leading unimodal and multimodal survival prediction techniques in both accuracy and interpretability, providing a new perspective on prototype learning for critical medical applications. Our source code is available at https://github.com/JSLiam94/FeatProto.

Momentum

Related topics

Latest papers 156

Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification

Explainability in mulimodal deep transformation models for stroke outcome prediction

Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations

Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration

Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images