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

Beyond Independent Frames: Latent Attention Masked Autoencoders for Multi-View Echocardiography
Apr 16, 2026Simon Böhi, Irene Cannistraci, Sergio Muñoz Gonzalez +8Medical Imaging Foundation Models Masked Autoencoders
Department of Biomedical Engineering, University of Basel, Switzerland · Department of Computer Science, ETH Zurich, Switzerland
Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images
Apr 16, 2026Jue Jiang, Aneesh Rangnekar, Harini VeeraraghavanMasked Autoencoders Self-Supervised Pre-Training
Memorial Sloan Kettering Cancer Center
Towards Interpretable Foundation Models for Retinal Fundus Images
Mar 19, 2026Samuel Ofosu Mensah, Camila Roa, Kerol Djoumessi +1Medical Imaging Medical Imaging Foundation Models
Hertie Institute for AI in Brain Health, University of Tübingen, Germany · Tübingen AI Center, University of Tübingen, Germany
Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast
Mar 4, 2026Mehmet Yigit Avci, Akshit Achara, Andrew King +1Medical Image Analysis Algorithmic Bias
and for the Alzheimer's Disease Neuroimaging Initiative · School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
Differential privacy representation geometry for medical image analysis
Mar 1, 2026Soroosh Tayebi Arasteh, Marziyeh Mohammadi, Sven Nebelung +1Neural Representation Geometry Healthcare
Lab for AI in Medicine, RWTH Aachen University, Aachen, Germany · Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany · INDA Institute, RWTH Aachen University, Aachen, Germany
US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound
Feb 22, 2026Ashwath Radhachandran, Vedrana Ivezić, Shreeram Athreya +2Joint-Embedding Predictive Architecture Ultrasound Imaging
Biomedical Artificial Intelligence Research Lab, University of California, Los Angeles, Los Angeles, United States of America · Medical Informatics, University of California, Los Angeles, Los Angeles, United States of America · Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, United States of America +3
MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis
Feb 19, 2026Junkai Liu, Ling Shao, Le ZhangDisentangled Representation Learning Self-Supervised Pre-Training
School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, England, United Kingdom · UCAS-Terminus AI Laboratory, University of Chinese Academy of Sciences, Beijing 100049, China
RadJEPA: Radiology Encoder for Chest X-Rays via Joint Embedding Predictive Architecture
Jan 22, 2026Anas Anwarul Haq Khan, Mariam Husain, Pratik Jalan +1Radiology Report Generation Medical Imaging
Department of Computer Science (CSE), Indian Institute of Technology Bombay, India · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Department of Biomedical Engineering, Johns Hopkins University, USA +1
CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training
Jan 18, 2026Pralaypati Ta, Sriram Venkatesaperumal, Keerthi Ram +1Medical VLMs Histopathology Image Classification
Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology, Madras, Chennai, India. · Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, India.
Stochastic Siamese MAE Pretraining for Longitudinal Medical Images
Dec 29, 2025Taha Emre, Arunava Chakravarty, Thomas Pinetz +9Masked Autoencoders Self-Supervised Pre-Training
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria · Department of Ophthalmology and Optometry, Medical University of Vienna, Austria · BioMedIA, Department of Computing, Imperial College London, London, United Kingdom +8
Diverse Normal Prototypes-Guided Contrastive Reconstruction for Medical Anomaly Detection
Aug 27, 2025Luhu Li, Bin Liu, Bowen Lin +3Prototype Learning Medical Image Anomaly Detection
School of Mathematics, Shandong University, Jinan 250100, China · Department of Interventional Medicine and Minimally Invasive Oncology, The Second Qilu Hospital of Shandong University, Jinan 250033, China · Department of Neurosurgery, The Second Qilu Hospital of Shandong University,2026 Jinan 250033, China
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.
Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets
May 27, 2025Alexander Jaus, Zdravko Marinov, Constantin Seibold +4Image Segmentation Medical Imaging Foundation Models
Stochastic Optimal Control for Continuous-Time fMRI Representation Learning
Feb 7, 2025Joonhyeong Park, Byoungwoo Park, Chang-Bae Bang +4Latent Dynamics Modeling Self-Supervised Time Series Representation Learning
KAIST · Yonsei University · EverEx +1
Full-resolution MLPs Empower Medical Dense Prediction
Nov 28, 2023Mingyuan Meng, Yuxin Xue, Dagan Feng +2Medical Image Registration Medical Image Segmentation
School of Computer Science, The University of Sydney, Australia. · Institute of Translational Medicine, Shanghai Jiao Tong University, China.
Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detection
Mar 3, 2022Qingsong Yao, Zhen Huang, Ao Wang +4Self-Supervised Learning Image Matching
Soochow School of Artificial Intelligence Renmin University of China · Department of Computer Science University of Science and Technology of China · School of Biomedical Engineering University of Science and Technology of China +3
PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images
Date pendingYang Luo, Shiru Wang, Jun Liu +7Computational Pathology Cancer Survival Prediction
XJLTU · Dartmouth · NUP +7
Representation learning of human cortical folding to reveal long lasting neurodevelopmental signatures
Date pendingJulien Laval, Robin Guiavarch, Antoine Dufournet +24Representation Learning Self-Supervised Learning
Universit´e Paris-Saclay, CEA, CNRS, Neurospin, UMR 9027 BAOBAB, GAIA Laboratory, Gif-sur-Yvette, France · LTCI, Institut Polytechnique de Paris, T´el´ecom Paris, Palaiseau, France · Maison de la simulation, Universit´e Paris-Saclay, CEA, Gif-sur-Yvette, France +10

Apr 16, 2026, cs.CVJ/K move · Enter open · S save

Beyond Independent Frames: Latent Attention Masked Autoencoders for Multi-View Echocardiography

Simon Böhi, Irene Cannistraci, Sergio Muñoz Gonzalez, Moritz Vandenhirtz+7

Department of Biomedical Engineering, University of Basel, Switzerland · Department of Computer Science, ETH Zurich, Switzerland

Medical Imaging Foundation Models Masked Autoencoders Multi-View Learning Echocardiography Ultrasound Imaging Medical Image Representation Learning

Open paper AskPDF arXiv

Echocardiography is a widely used modality for cardiac assessment due to its non-invasive and cost-effective nature, but the sparse and heterogeneous spatiotemporal views of the heart pose distinct challenges. Existing masked autoencoder (MAE) approaches typically process images or short clips independently, failing to capture the inherent multi-view structure required for coherent cardiac representation. We introduce Latent Attention Masked Autoencoder (LAMAE), a foundation model architecture tailored to the multi-view nature of medical imaging. LAMAE augments the standard MAE with a latent attention module that enables information exchange across frames and views directly in latent space. This allows the model to aggregate variable-length sequences and distinct views, reconstructing a holistic representation of cardiac function from partial observations. We pretrain LAMAE on MIMIC-IV-ECHO, a large-scale, uncurated dataset reflecting real-world clinical variability. To the best of our knowledge, we present the first results for predicting ICD-10 codes from MIMIC-IV-ECHO videos. Furthermore, we empirically demonstrate that representations learned from adult data transfer effectively to pediatric cohorts despite substantial anatomical differences. These results provide evidence that incorporating structural priors, such as multi-view attention, yields significantly more robust and transferable representations.

Momentum

Related topics

Latest papers 168