Medical Image Representation Learning

Follow this topic Ask about this topic

Momentum

20 papers in the last four weeks, up 233% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 168

All topics

Learning Task-Aware Sampling with Shared Saliency through Density-Equalizing Mappings
Jun 11, 2026Tsz Lok Ip, Han Zhang, Lok Ming LuiGeometric Representation Learning Convolutional Neural Networks
Department of Mathematics, The Chinese University of Hong Kong, Hong Kong, China. · Department of Mathematics, City University of Hong Kong, Hong Kong, China.
Frozen Foundation-Model Embeddings Discard Small-Lesion Signal in Chest Radiography: Implications for Pre-Deployment Evaluation
Jun 10, 2026Raajitha Muthyala, Zhenan Yin, Alekhya Jilla +6Chest X-Ray Classification Medical Imaging Foundation Models
Department of Biomedical Engineering and Informatics, Indiana University, 535 W Michigan St., IT 475J, Indianapolis, IN, 46202, USA · Department of Radiology and Imaging Sciences, Emory University, 1364 Clifton Rd NE, Atlanta, GA, 30322, USA
FlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI
Jun 9, 2026Mo Wang, Wenhao Ye, Junfeng Xia +3Medical Imaging Foundation Models fMRI Foundation Models
Southern University of Science and Technology · University of Warwick
CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs
Jun 7, 2026Sergios Gatidis, Curtis Langlotz, Christian BluethgenRadiology VLMs Medical Image Segmentation
Stanford Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Palo Alto, CA, USA · Department of Radiology, Stanford University, Stanford, CA, USA
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
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
EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models
Jun 4, 2026Hao Wang, Qiwei Zeng, Jinghao Lin +6VLM Adaptation Medical Image Anomaly Detection
School of Computer Science, The University of Sydney, Sydney, NSW, Australia · Jilin University, Changchun, China · Northeastern University, Shenyang, China +1
CDPM-Align: Multi-Scale Guidance-Aligned Diffusion Pretraining for Robust Few-Shot Anatomical Landmark Detection
Jun 3, 2026Roberto Di Via, Irina Voiculescu, Francesca Odone +1Diffusion Model Alignment Diffusion Model Guidance
MaLGa, DIBRIS, University of Genoa, Italy · Department of Computer Science, University of Oxford, UK
StrokeTimer: Robust Representation Learning for Ischemic Stroke Onset-Time Estimation from Non-contrast CT
Jun 3, 2026Weiru Wang, Susanne G. H. Olthuis, Elizaveta Lavrova +4Medical Image Analysis Medical Image Representation Learning
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands · Graduate School of Life Sciences, Utrecht University, Utrecht, The Netherlands · Department of Neurology, Maastricht University Medical Centre+, Maastricht, The Netherlands +3
GLINT: Sparsely Gated Vision-Language Alignment for Fine-Grained Radiology Representations
Jun 2, 2026Jonggwon Park, Seongeun Lee, Junhyun Park +6Radiology VLMs Medical Image Segmentation
DEEPNOID Inc., Seoul, South Korea · Department of Artificial Intelligence and Data Science, Sejong University, Seoul, South Korea
PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder
Jun 1, 2026Yancheng Liu, Kenichi Maeda, Manan PancholyCross-Modal Knowledge Distillation Chest X-Ray Classification
Department of Computer Science, Brown University, Providence, RI, USA.
Bridging Topology and Deep Representation Learning: A TDA-ViT Fusion Model for Four-Class Brain Tumor Classification
May 30, 2026Faisal AhmedVision Transformer Medical Image Classification
Department of Data Science and Mathematics, Embry-Riddle Aeronautical University, 3700 Willow Creek Rd, Prescott, 86301, Arizona, USA.
hZACH-ViT: Curved Latent Geometry for Compact Vision Transformers in Low-Data Medical Imaging
May 30, 2026Athanasios AngelakisGeometric Representation Learning Efficient ViTs
BioML Lab, Research Institute CODE, UniBw, Munich, Germany · Department of Epidemiology and Data Science, Amsterdam UMC, Amsterdam, Netherlands
Cohort-Scale Neural Atlases of Ultrasound Video
May 30, 2026Zhuorui Zhang, Roger Pallarès-López, Xuan Wu +2Medical Image Registration Ultrasound Imaging
Department of Mechanical Engineering, MIT · Institute for Medical Engineering and Science, MIT · 3MIT.nano Immersion Lab, MIT
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
Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts
May 29, 2026Honglin Xiong, Yuxian Tang, Feng Li +4Disentangled Representation Learning Magnetic Resonance Imaging
Genetically Aligned Patient Representations Improve Hematological Diagnosis
May 28, 2026Muhammed Furkan Dasdelen, Fatih Ozlugedik, Ilaria Looser +3Computational Pathology Healthcare
Institute of AI for Health, Helmholtz Munich, Germany · International School of Medicine, Istanbul Medipol University, Türkiye · Munich Leukemia Laboratory, Germany +4
Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning
May 27, 2026Jiyao Wang, Peiyu Duan, Nicha C. Dvornek +4Self-Supervised Learning Siamese Neural Networks
Department of Biomedical Engineering, Yale University, New Haven, CT, USA · Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA · Electrical Engineering, Yale University, New Haven, CT, USA +1
A multifractal-based masked auto-encoder: an application to medical images
May 25, 2026Joao Batista Florindo, Viviane de MouraMasked Autoencoders Medical Image Classification
Institute of Mathematics, Statistics and Scientific Computing - University of Campinas, Rua Sérgio Buarque de Holanda, 651, Cidade Universitária ”Zeferino Vaz” - Distr. Barão Geraldo, CEP 13083-859, Campinas, SP, Brasil
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.
Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representation
May 25, 2026Chunzheng Zhu, Yijun Wang, Jianxin Lin +5Invariant Representation Learning Medical Image Representation Learning
Hunan University, Changsha, China · Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, China
CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval
May 22, 2026Zahra Rahimi Afzal, Wataru Uegami, Saghir Alfasly +6Computational Pathology Medical Image Representation Learning
Kimia Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA · DICE Lab, Department of Electrical and Computer Engineering, University of Illinois Chicago, IL, USA · Division of Computational Pathology and Informatics, Mayo Clinic, Rochester, MN, USA +4
MuellerPT: Decomposition Driven Pretraining for Dense Learning in Mueller Polarimetry
May 22, 2026Adam Tlemsani, Yingdian Li, Maxime Giot +4Self-Supervised Pre-Training Medical Image Classification
Department of Computing, Imperial College London · Hamlyn Centre for Robotic Surgery, Imperial College London · Department of Surgery and Cancer, Imperial College London +2
Entropy-Guided Self-Supervised Learning for Medical Image Classification
May 21, 2026Joao Florindo, Viviane MouraSelf-Supervised Pre-Training Medical Image Classification
Institute of Mathematics, Statistics and Scientific Computing Department of Applied Mathematics, University of Campinas Rua Sergio Buarque de Holanda, 651, Campinas, 13083-859, Sao Paulo, Brazil
Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining
May 21, 2026Yuheng Li, Yuan Gao, Haoyu Dong +5Medical Imaging Foundation Models Medical VLMs
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA · Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA · Department of Electrical and Computer Engineering, Duke University, Durham, NC 27705, USA +2
Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models
May 21, 2026Yuting He, Chenyu You, Shuo LiMedical Imaging Foundation Models Multimodal Pretraining
Case Western Reserve University · Stony Brook University
Automatic Discovery of Disease Subgroups by Contrasting with Healthy Controls
May 20, 2026Robin Louiset, Edouard Duchesnay, Benoit Dufumier +2Contrastive Learning Healthcare
1NeuroSpin, Universit´e Paris-Saclay, CEA, Gif-sur-Yvette, 91191, France. · 2LTCI, Institut Polytechnique de Paris, T´el´ecom Paris, Palaiseau,2026 91120, France.
Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines
May 18, 2026Chathura Wimalasiri, Kishor Nandakishor, Marimuthu PalaniswamiContrastive Learning Self-Supervised Learning
Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3052, Australia
A World Model of Radiologist Reading for Medical Image Representation Learning
May 17, 2026Yiwei Li, Zihao Wu, Huaqin Zhao +5World Model Learning Healthcare
University of Georgia · University of Texas at Arlington · New Jersey Institute of Technology
The Learnability Gap in Medical Latent Diffusion
May 16, 2026Mischa Dombrowski, Felix Nützel, Bernhard KainzLatent Diffusion Models Medical Image Classification
FAU Erlangen-Nürnberg, Erlangen, DE · Department of Computing, Imperial College London, London, UK

Jun 11, 2026, cs.CVJ/K move · Enter open · S save

Learning Task-Aware Sampling with Shared Saliency through Density-Equalizing Mappings

Tsz Lok Ip, Han Zhang, Lok Ming Lui

Department of Mathematics, The Chinese University of Hong Kong, Hong Kong, China. · Department of Mathematics, City University of Hong Kong, Hong Kong, China.

Geometric Representation Learning Convolutional Neural Networks Medical Image Representation Learning

Open paper AskPDF arXiv

In image and surface-based learning tasks, convolutional features are typically extracted using receptive fields that are sampled uniformly across the entire domain. However, informative structures are rarely distributed uniformly in practice and are often concentrated in localized regions. Such phenomena are particularly common in medical imaging, where pathological changes are spatially confined. Consequently, uniform convolution allocates equal computational effort to both informative and uninformative regions, resulting in inefficient feature extraction and suboptimal utilization of model capacity. To address this issue, we propose a framework for task-adaptive sampling that dynamically redistributes computational attention according to the spatial importance of the data. Specifically, we introduce the Density-Equalizing Convolutional Neural Network (DECNN), which employs density-equalizing mappings to guide convolution through a learned density function. The density function encodes the relative importance of different regions and induces a transformation that enlarges informative areas while compressing less relevant ones. As a result, convolutional receptive fields are redistributed non-uniformly over the domain, enabling denser sampling in task-relevant regions. By coupling this importance-driven transformation with convolution, DECNN performs adaptive feature extraction that focuses computational resources on informative structures. This leads to more efficient use of model capacity, yielding a lightweight yet expressive architecture while simultaneously producing an interpretable saliency map. Experiments on image classification and craniofacial surface analysis demonstrate that DECNN achieves competitive or superior performance with fewer parameters, accurately identifies task-relevant regions, and remains robust under complex geometric variations.

Momentum

Related topics

Latest papers 168