{"total":69,"rows":[{"id":"2307.01849","title":"Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning","created":"2023-07-04","cited_by_count":40,"area":"diffusion"},{"id":"2404.09957","title":"How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model","created":"2024-04-15","cited_by_count":32,"area":null},{"id":"2411.02372","title":"Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis","created":"2024-11-04","cited_by_count":29,"area":null},{"id":"2405.05237","title":"EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised Learning","created":"2024-05-08","cited_by_count":25,"area":null},{"id":"2402.18922","title":"A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection","created":"2024-02-29","cited_by_count":23,"area":null},{"id":"2504.13820","title":"CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning","created":"2025-04-18","cited_by_count":19,"area":"wm"},{"id":"2411.04656","title":"ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism","created":"2024-11-07","cited_by_count":16,"area":null},{"id":"2410.01665","title":"Towards a vision foundation model for comprehensive assessment of Cardiac MRI","created":"2024-10-02","cited_by_count":14,"area":null},{"id":"2504.13037","title":"Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond","created":"2025-04-17","cited_by_count":14,"area":null},{"id":"2309.06828","title":"UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training","created":"2023-09-13","cited_by_count":13,"area":null},{"id":"2408.05815","title":"HySparK: Hybrid Sparse Masking for Large Scale Medical Image Pre-Training","created":"2024-08-11","cited_by_count":12,"area":null},{"id":"2502.02779","title":"3D Foundation Model for Generalizable Disease Detection in Head Computed Tomography","created":"2025-02-04","cited_by_count":12,"area":null},{"id":"2412.19237","title":"SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensing","created":"2024-12-26","cited_by_count":11,"area":null},{"id":"2407.06468","title":"AnatoMask: Enhancing Medical Image Segmentation with Reconstruction-guided Self-masking","created":"2024-07-09","cited_by_count":10,"area":null},{"id":"2406.00329","title":"Whole Heart 3D+T Representation Learning Through Sparse 2D Cardiac MR Images","created":"2024-06-01","cited_by_count":9,"area":null},{"id":"2408.08070","title":"MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation","created":"2024-08-15","cited_by_count":7,"area":"ssm"},{"id":"2310.16318","title":"Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder","created":"2023-10-25","cited_by_count":5,"area":null},{"id":"2402.03752","title":"Pre-training of Lightweight Vision Transformers on Small Datasets with Minimally Scaled Images","created":"2024-02-06","cited_by_count":4,"area":null},{"id":"2601.00260","title":"TotalFM: An Organ-Separated 3D-CT Foundation Model Leveraging Large-Scale Routine Clinical Radiology Data","created":"2026-01-01","cited_by_count":4,"area":null},{"id":"2404.08915","title":"PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification","created":"2024-04-13","cited_by_count":3,"area":null},{"id":"2407.16264","title":"Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring","created":"2024-07-23","cited_by_count":3,"area":null},{"id":"2408.12814","title":"From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels","created":"2024-08-23","cited_by_count":3,"area":null},{"id":"2505.00568","title":"Multimodal Masked Autoencoder Pre-training for 3D MRI-Based Brain Tumor Analysis with Missing Modalities","created":"2025-05-01","cited_by_count":3,"area":null},{"id":"2509.08311","title":"SimCroP: Radiograph Representation Learning with Similarity-driven Cross-granularity Pre-training","created":"2025-09-10","cited_by_count":3,"area":"mllm"},{"id":"2510.14532","title":"Towards Generalist Intelligence in Dentistry: Vision Foundation Models for Oral and Maxillofacial Radiology","created":"2025-10-16","cited_by_count":3,"area":null},{"id":"2511.01284","title":"Adaptation of Foundation Models for Medical Image Analysis: Strategies, Challenges, and Future Directions","created":"2025-11-03","cited_by_count":3,"area":null},{"id":"2603.15553","title":"Self-Distillation of Hidden Layers for Self-Supervised Representation Learning","created":"2026-03-16","cited_by_count":3,"area":"distill"},{"id":"2411.15490","title":"Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation","created":"2024-11-23","cited_by_count":2,"area":null},{"id":"2501.00420","title":"KAE: Kolmogorov-Arnold Auto-Encoder for Representation Learning","created":"2024-12-31","cited_by_count":2,"area":null},{"id":"2501.09096","title":"Self Pre-training with Adaptive Mask Autoencoders for Variable-Contrast 3D Medical Imaging","created":"2025-01-15","cited_by_count":2,"area":null},{"id":"2510.22527","title":"Multi-Modal Masked Autoencoders for Learning Image-Spectrum Associations for Galaxy Evolution and Cosmology","created":"2025-10-26","cited_by_count":2,"area":null},{"id":"2203.16983","title":"Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification","created":"2022-03-31","cited_by_count":1,"area":"distill"},{"id":"2402.04587","title":"Sparse Anatomical Prompt Semi-Supervised Learning with Masked Image Modeling for CBCT Tooth Segmentation","created":"2024-02-07","cited_by_count":1,"area":null},{"id":"2407.20878","title":"S3PET: Semi-supervised Standard-dose PET Image Reconstruction via Dose-aware Token Swap","created":"2024-07-30","cited_by_count":1,"area":null},{"id":"2504.10021","title":"Masked Autoencoder Self Pre-Training for Defect Detection in Microelectronics","created":"2025-04-14","cited_by_count":1,"area":null},{"id":"2508.04429","title":"Unmasking Interstitial Lung Diseases: Leveraging Masked Autoencoders for Diagnosis","created":"2025-08-06","cited_by_count":1,"area":null},{"id":"2509.09064","title":"Enhancing 3D Medical Image Understanding with Pretraining Aided by 2D Multimodal Large Language Models","created":"2025-09-11","cited_by_count":1,"area":"mllm"},{"id":"2509.11442","title":"MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal Masked Autoencoder","created":"2025-09-14","cited_by_count":1,"area":null},{"id":"2601.18532","title":"From Cold Start to Active Learning: Embedding-Based Scan Selection for Medical Image Segmentation","created":"2026-01-26","cited_by_count":1,"area":null},{"id":"2605.21070","title":"Towards Understanding Self-Pretraining for Sequence Classification","created":"2026-05-20","cited_by_count":1,"area":null},{"id":"2606.00602","title":"ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training","created":"2026-05-30","cited_by_count":1,"area":"mllm"},{"id":"2309.04312","title":"AMLP: Adjustable Masking Lesion Patches for Self-Supervised Medical Image Segmentation","created":"2023-09-08","cited_by_count":0,"area":null},{"id":"2403.02307","title":"Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection","created":"2024-03-04","cited_by_count":0,"area":null},{"id":"2403.04626","title":"MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder","created":"2024-03-07","cited_by_count":0,"area":null},{"id":"2405.07411","title":"MoVL:Exploring Fusion Strategies for the Domain-Adaptive Application of Pretrained Models in Medical Imaging Tasks","created":"2024-05-13","cited_by_count":0,"area":"mechinterp"},{"id":"2407.00431","title":"Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones","created":"2024-06-29","cited_by_count":0,"area":null},{"id":"2502.21244","title":"Anatomically-guided masked autoencoder pre-training for aneurysm detection","created":"2025-02-28","cited_by_count":0,"area":null},{"id":"2504.03589","title":"AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities","created":"2025-04-04","cited_by_count":0,"area":null},{"id":"2504.17935","title":"Masked strategies for images with small objects","created":"2025-04-24","cited_by_count":0,"area":null},{"id":"2505.08693","title":"VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation","created":"2025-05-13","cited_by_count":0,"area":null},{"id":"2506.11674","title":"Cross-Modal Clustering-Guided Negative Sampling for Self-Supervised Joint Learning from Medical Images and Reports","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2507.10464","title":"AudioMAE++: learning better masked audio representations with SwiGLU FFNs","created":"2025-07-14","cited_by_count":0,"area":null},{"id":"2507.20056","title":"FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation","created":"2025-07-26","cited_by_count":0,"area":"ssm"},{"id":"2508.18613","title":"ModAn-MulSupCon: Modality-and Anatomy-Aware Multi-Label Supervised Contrastive Pretraining for Medical Imaging","created":"2025-08-26","cited_by_count":0,"area":null},{"id":"2508.20622","title":"Masked Autoencoders for Ultrasound Signals: Robust Representation Learning for Downstream Applications","created":"2025-08-28","cited_by_count":0,"area":null},{"id":"2509.15272","title":"Which Direction to Choose? An Analysis on the Representation Power of Self-Supervised ViTs in Downstream Tasks","created":"2025-09-18","cited_by_count":0,"area":null},{"id":"2510.14657","title":"Decorrelation Speeds Up Vision Transformers","created":"2025-10-16","cited_by_count":0,"area":null},{"id":"2511.04811","title":"An Active Learning Pipeline for Biomedical Image Instance Segmentation with Minimal Human Intervention","created":"2025-11-06","cited_by_count":0,"area":null},{"id":"2603.05535","title":"Clinical-Injection Transformer with Domain-Adapted MAE for Lupus Nephritis Prognosis Prediction","created":"2026-03-04","cited_by_count":0,"area":null},{"id":"2603.09945","title":"No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2603.14271","title":"Toward Clinically Ready Foundation Models in Medical Image Analysis: Adaptation Mechanisms and Deployment Trade-offs","created":"2026-03-15","cited_by_count":0,"area":null},{"id":"2604.15711","title":"SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification","created":"2026-04-17","cited_by_count":0,"area":null},{"id":"2605.00448","title":"Learning from Compressed CT: Feature Attention Style Transfer and Structured Factorized Projections for Resource-Efficient Medical Image Analysis","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2606.01537","title":"PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2606.03508","title":"Structure-Guided Mixed Masked Pretraining and Spatial Continuity Regularization for Printed Circuit Board Defect Detection","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.23964","title":"3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2607.00850","title":"Mirror-Fusion Attention for Reflection-Aware Self-Supervised Representation Learning","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2608.00446","title":"Structured Proxy Features for Multimodal NSCLC Survival Prediction from Pretreatment CT","created":"2026-08-01","cited_by_count":0,"area":null},{"id":"2608.06122","title":"Is Self-Pretraining really useful to improve diagnosis in medical Time Series?","created":"2026-08-06","cited_by_count":0,"area":null}],"next_cursor":null}