Masked Autoencoders

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13 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Contrastive and Adaptive Multi-modal Masked Autoencoder for Spatial Transcriptomics
Jun 19, 2026Joohyeok Kim, Taejin Jeong, Jinyeong Kim +1Computational Pathology Spatial Transcriptomics
Department of Artificial Intelligence, Yonsei University, Seoul, Republic of Korea · Department of Computer Science, Yonsei University, Seoul, Republic of Korea · Yonsei University College of Medicine, Seoul, Republic of Korea
BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation
Jun 17, 2026Max Van Puyvelde, Ibrahim Gulluk, Wim Van Criekinge +1Masked Autoencoders Latent Diffusion Models
Department of Biomedical Data Science, Stanford University School of Medicine · Department of Mathematical Modelling, Statistics & Bioinformatics, Ghent University · Department of Electrical Engineering, Stanford University
Masked and Predictive Self-Supervised Foundation Models for 3D Brain MRI
Jun 11, 2026Esra Ergün, Hersh Chandarana, Dan Sodickson +1Medical Imaging Foundation Models Masked Autoencoders
Department of Computer Engineering, Istanbul Technical University, Istanbul, 34469, Türkiye · Department of Radiology, NYU Grossman School of Medicine, New York, NY, 10016, United States · Department of AI and Data Engineering, Istanbul Technical University, Istanbul, 34469, Türkiye
RePAIR: Predictive Self-Supervised Representation Learning in Chess
Jun 10, 2026Christoph Koller, Johannes Fürnkranz, Timo BertramMasked Autoencoders Joint-Embedding Predictive Architecture
JKU Linz, Austria
Exploring Adaptive Masked Reconstruction for Self-Supervised Skeleton-Based Action Recognition
Jun 9, 2026Shengkai Sun, Zhiyong Cheng, Zefan Zhang +3Skeleton-Based Action Recognition Masked Autoencoders
1Hefei University of Technology · 2Jilin University · 3Zhejiang Gongshang University +1
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.
Effective Biological Representation Learning by Masking Gene Expression
May 29, 2026Kian Kenyon-Dean, Alina Selega, Ihab Bendidi +5Masked Autoencoders Self-Supervised Learning
Recursion · Valence Labs · École Normale Supérieure PSL
FHRFormer: A Self-Supervised Masked Transformer Framework for Fetal Heart Rate Time-Series Inpainting and Forecasting
May 28, 2026Kjersti Engan, Neel Kanwal, Anita Yeconia +4Time Series Imputation Masked Autoencoders
University of Stavanger, Stavanger, Norway · Haydom Lutheran Hospital, Haydom, Tanzania · Stavanger University Hospital, Stavanger, Norway
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
Semantics-Guided Multimodal Masked Autoencoder Pretraining for 3D BEV Object Detection
May 24, 2026Prabuddhi Wariyapperuma, Rajitha de Silva, Marc Hanheide +2Lidar-Based 3D Object Detection Masked Autoencoders
University of Lincoln, Lincoln Centre for Autonomous Systems, Lincoln, UK. · University of Cambridge, Institute for Manufacturing, Department of Engineering, Cambridge, UK.
BandVQ: Band-Wise Vector-Quantized EEG Foundation Model
May 24, 2026Jamiyan Sukhbaatar, Satoshi Imamura, Toshihisa TanakaCross-Subject EEG Decoding Masked Autoencoders
National University of Mongolia, Ulaanbaatar, Mongolia · Tokyo University of Agriculture and Technology, Tokyo, Japan
PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels
May 19, 2026Berkay Guler, Giovanni Geraci, Hamid JafarkhaniRepresentation Learning Masked Autoencoders
Center for Pervasive Communications and Computing, University of California, Irvine CA, USA · Nokia and Universitat Pompeu Fabra, Spain
Protein Fold Classification at Scale: Benchmarking and Pretraining
May 18, 2026Dexiong Chen, Andrei Manolache, Mathias Niepert +1Masked Autoencoders Self-Supervised Pre-Training
Max Planck Institute of Biochemistry, Martinsried, Germany · Computer Science Department, University of Stuttgart, Germany · Bitdefender, Romania
LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift
May 18, 2026Haozhe Si, Yuxuan Wan, Yuqing Wang +2Representation Learning Masked Autoencoders
Department of Electrical and Computer Engineering · 2Siebel School of Computing and Data Science
DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG
May 18, 2026Yang Shao, Peiliang Gong, Qun Dai +1Masked Autoencoders Self-Supervised Pre-Training
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics Nanjing, Jiangsu, China
Pretraining Objective Matters in Extreme Low-Data FGVC: A Backbone-Controlled Study
May 15, 2026Alexander Hackett, Srikanth Thudumu, Ginny Fisher +1Contrastive Learning Masked Autoencoders
Santa Clara University · IAAIR
Network-Aware Bilinear Tokenization for Brain Functional Connectivity Representation Learning
May 13, 2026Leo Milecki, Qingyu Hu, Bahram Jafrasteh +2Representation Learning Masked Autoencoders
Department of Radiology, Weill Cornell Medicine, New York, NY, USA. · School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA.
Masked Autoencoders with Limited Data: Does It Work? A Fine-Grained Bioacoustics Case Study
May 13, 2026Wuao Liu, Mustafa Chasmai, Subhransu Maji +1Masked Autoencoders Bioacoustics
University of Massachusetts Amherst
BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability
May 13, 2026Guangqian Yang, Tong Ding, Wenlong Hou +4Missing-Modality Learning Masked Autoencoders
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China · Department of Technology Management for Innovation, The University of Tokyo, Japan · Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR, China
Biosignal Fingerprinting: A Cross-Modal PPG-ECG Foundation Model
May 10, 2026Zhangdaihong Liu, Chang Liu, Fenglin Liu +4Cross-Modal Representation Learning Masked Autoencoders
Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK · Oxford Suzhou Centre for Advanced Research, Suzhou, 215123, China · School of Public Health, Shanghai Jiao Tong University, Shanghai, China
OphMAE: Bridging Volumetric and Planar Imaging with a Foundation Model for Adaptive Ophthalmological Diagnosis
May 4, 2026Tienyu Chang, Zhen Chen, Renjie Liang +9Medical Imaging Foundation Models Masked Autoencoders
Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN · Department of Biomedical Informatics and Data Science, Yale University, New Haven, CT · Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong +5
Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning
May 1, 2026Hao Zhou, Simon A. Lee, Cyrus Tanade +12Representation Learning Cross-Modal Representation Learning
Samsung Research America · Algorithm Team, Digital Health Lab, Samsung Research America · The Pennsylvania State University +1
MAEPose: Self-Supervised Spatiotemporal Learning for Human Pose Estimation on mmWave Video
Apr 30, 2026Xijia Wei, Yuan Fang, Kevin Chetty +2Masked Autoencoders Self-Supervised Learning
University College London London, UK
Do Masked Autoencoders Improve Downhole Prediction? An Empirical Study on Real Well Drilling Data
Apr 21, 2026Aleksander Berezowski, Hassan Hassanzadeh, Gouri GindeMasked Autoencoders Self-Supervised Time Series Representation Learning
Schulich School of Engineering Department of Electrical and Software Engineering University of Calgary Calgary, Canada · Schulich School of Engineering Department of Chemical and Petroleum Engineering University of Calgary Calgary, Canada
Better with Less: Tackling Heterogeneous Multi-Modal Image Joint Pretraining via Conditioned and Degraded Masked Autoencoder
Apr 18, 2026Bowen Peng, Yongxiang Liu, Jie Zhou +4Cross-Modal Learning Masked Autoencoders
College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha 410073, China · Beijing Institute of Remote Sensing Information, Beijing 100192, China
PolarMAE: Efficient Fetal Ultrasound Pre-training via Semantic Screening and Polar-Guided Masking
Apr 17, 2026Meng Lv, Yapeng Li, Hang Su +2Masked Autoencoders Self-Supervised Pre-Training
Wuhan University
Assessing the Potential of Masked Autoencoder Foundation Models in Predicting Downhole Metrics from Surface Drilling Data
Apr 16, 2026Aleksander Berezowski, Hassan Hassanzadeh, Gouri GindeMultivariate Time Series Forecasting Masked Autoencoders
Schulich School of Engineering Department of Electrical and Software Engineering University of Calgary Calgary, Canada · Schulich School of Engineering Department of Chemical and Petroleum Engineering University of Calgary Calgary, Canada
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
VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection
Mar 27, 2026PengYu Chen, Shang Wan, Xiaohou Shi +3Masked Autoencoders Vision Foundation Model Adaptation
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China · China Telecom Research Institute Beijing, China · Department of Computer Science, Missouri University of Science and Technology, Rolla, MO 65409 USA

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Contrastive and Adaptive Multi-modal Masked Autoencoder for Spatial Transcriptomics

Joohyeok Kim, Taejin Jeong, Jinyeong Kim, Seong Jae Hwang

Department of Artificial Intelligence, Yonsei University, Seoul, Republic of Korea · Department of Computer Science, Yonsei University, Seoul, Republic of Korea · Yonsei University College of Medicine, Seoul, Republic of Korea

Computational Pathology Spatial Transcriptomics Masked Autoencoders Spatial Gene Expression Prediction Gene Expression Prediction from Histopathology Images Whole-Slide Image Analysis

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The high cost of spatial transcriptomics (ST) has driven extensive studies into predicting gene expression directly from H&E histology images. However, this prediction task faces an inherent limitation, as tissue morphology alone provides insufficient information to fully resolve underlying gene expression. To address this limitation, a recent study leverages partial gene expression to guide the prediction process alongside histology images. Building on this paradigm, we approach the prediction task as a spatial imputation problem, employing a Masked Autoencoder (MAE) to utilize a small fraction of gene expression as genetic anchors for inferring whole-slide gene expression profiles. Specifically, we propose a bio-saliency score and a learning-to-rank strategy to adaptively identify the most informative spots within the tissue. Based on these identified spots, our framework selects contiguous regions as genetic anchors to ensure suitability for real-world ST profiling hardware. To effectively leverage these anchors, we design a cross-modal joint encoder that integrates visual and genetic modalities. By aligning the selected anchors with their corresponding visual features via contrastive learning, the encoder generates robust joint representations to accurately predict gene expression across the whole slide. Notably, our framework consistently surpasses existing methods in both histology-only prediction and spatial imputation, achieving superior accuracy even without genetic anchors and further excelling with as little as 10% transcriptomic coverage. Our code is available at https://github.com/Kyyle2114/CAMMST.

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