{"total":82,"rows":[{"id":"2306.05705","title":"On the Challenges and Perspectives of Foundation Models for Medical Image Analysis","created":"2023-06-09","cited_by_count":135,"area":null},{"id":"2405.12971","title":"BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once","created":"2024-05-21","cited_by_count":111,"area":null},{"id":"2404.03264","title":"Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions","created":"2024-04-04","cited_by_count":77,"area":null},{"id":"2406.10729","title":"A Comprehensive Survey of Foundation Models in Medicine","created":"2024-06-15","cited_by_count":56,"area":null},{"id":"2304.14660","title":"Segment Anything Model for Medical Images?","created":"2023-04-28","cited_by_count":39,"area":null},{"id":"2309.13401","title":"Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals","created":"2023-09-23","cited_by_count":25,"area":null},{"id":"2305.02644","title":"Neuralizer: General Neuroimage Analysis without Re-Training","created":"2023-05-04","cited_by_count":24,"area":null},{"id":"2405.01002","title":"Spider: A Unified Framework for Context-dependent Concept Segmentation","created":"2024-05-02","cited_by_count":18,"area":null},{"id":"2307.07044","title":"AnyStar: Domain randomized universal star-convex 3D instance segmentation","created":"2023-07-13","cited_by_count":15,"area":null},{"id":"2405.10246","title":"A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts","created":"2024-05-16","cited_by_count":14,"area":null},{"id":"2406.07085","title":"CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor Segmentation","created":"2024-06-11","cited_by_count":14,"area":null},{"id":"2503.05618","title":"Conformal Prediction for Image Segmentation Using Morphological Prediction Sets","created":"2025-03-07","cited_by_count":14,"area":null},{"id":"2406.05565","title":"Medical Vision Generalist: Unifying Medical Imaging Tasks in Context","created":"2024-06-08","cited_by_count":13,"area":null},{"id":"2503.12781","title":"SAM2 for Image and Video Segmentation: A Comprehensive Survey","created":"2025-03-17","cited_by_count":13,"area":null},{"id":"2306.12153","title":"DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences","created":"2023-06-21","cited_by_count":10,"area":null},{"id":"2402.05035","title":"A Survey on Domain Generalization for Medical Image Analysis","created":"2024-02-07","cited_by_count":9,"area":null},{"id":"2412.01240","title":"Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes","created":"2024-12-02","cited_by_count":9,"area":null},{"id":"2405.18511","title":"Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation","created":"2024-05-28","cited_by_count":8,"area":null},{"id":"2505.09274","title":"Recent Advances in Medical Imaging Segmentation: A Survey","created":"2025-05-14","cited_by_count":8,"area":null},{"id":"2504.10979","title":"Deep Learning in Concealed Dense Prediction","created":"2025-04-15","cited_by_count":6,"area":null},{"id":"2403.05433","title":"Part-aware Prompted Segment Anything Model for Adaptive Segmentation","created":"2024-03-08","cited_by_count":5,"area":null},{"id":"2403.16578","title":"SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging","created":"2024-03-25","cited_by_count":5,"area":null},{"id":"2403.15901","title":"MatchSeg: Towards Better Segmentation via Reference Image Matching","created":"2024-03-23","cited_by_count":4,"area":null},{"id":"2407.20724","title":"Exploring Loss Landscapes through the Lens of Spin Glass Theory","created":"2024-07-30","cited_by_count":4,"area":null},{"id":"2409.19540","title":"LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models","created":"2024-09-29","cited_by_count":4,"area":null},{"id":"2503.04522","title":"ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality","created":"2025-03-06","cited_by_count":4,"area":null},{"id":"2307.03266","title":"Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging","created":"2023-07-06","cited_by_count":3,"area":null},{"id":"2404.17033","title":"Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation","created":"2024-04-25","cited_by_count":3,"area":null},{"id":"2405.16124","title":"Unsupervised Meta-Learning via In-Context Learning","created":"2024-05-25","cited_by_count":3,"area":null},{"id":"2412.04106","title":"MRGen: Segmentation Data Engine for Underrepresented MRI Modalities","created":"2024-12-04","cited_by_count":3,"area":null},{"id":"2412.13299","title":"In-context learning for medical image segmentation","created":"2024-12-17","cited_by_count":3,"area":null},{"id":"2504.16739","title":"Prompt-Tuning SAM: From Generalist to Specialist with only 2048 Parameters and 16 Training Images","created":"2025-04-23","cited_by_count":3,"area":null},{"id":"2504.18020","title":"Federated Client-tailored Adapter for Medical Image Segmentation","created":"2025-04-25","cited_by_count":3,"area":null},{"id":"2506.18679","title":"MARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentation","created":"2025-06-23","cited_by_count":3,"area":null},{"id":"2508.11032","title":"MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation","created":"2025-08-14","cited_by_count":3,"area":null},{"id":"2309.02742","title":"MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization","created":"2023-09-06","cited_by_count":2,"area":null},{"id":"2401.15526","title":"Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy","created":"2024-01-27","cited_by_count":2,"area":null},{"id":"2407.09561","title":"Integrating Deep Learning in Cardiology: A Comprehensive Review of Atrial Fibrillation, Left Atrial Scar Segmentation, and the Frontiers of State-of-the-Art Techniques","created":"2024-07-01","cited_by_count":2,"area":null},{"id":"2410.19283","title":"ST-NeRP: Spatial-Temporal Neural Representation Learning with Prior Embedding for Patient-specific Imaging Study","created":"2024-10-25","cited_by_count":2,"area":null},{"id":"2503.06743","title":"X-GAN: A Generative AI-Powered Unsupervised Model for Main Vessel Segmentation of Glaucoma Screening","created":"2025-03-09","cited_by_count":2,"area":null},{"id":"2505.22855","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2506.10825","title":"Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches","created":"2025-06-12","cited_by_count":2,"area":null},{"id":"2509.19711","title":"Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesis","created":"2025-09-24","cited_by_count":2,"area":null},{"id":"2306.14752","title":"MedLSAM: Localize and Segment Anything Model for 3D CT Images","created":"2023-06-26","cited_by_count":1,"area":null},{"id":"2312.06592","title":"Flexible visual prompts for in-context learning in computer vision","created":"2023-12-11","cited_by_count":1,"area":null},{"id":"2407.18100","title":"DINOv2 Rocks Geological Image Analysis: Classification, Segmentation, and Interpretability","created":"2024-07-25","cited_by_count":1,"area":null},{"id":"2410.09759","title":"Data Adaptive Few-shot Multi Label Segmentation with Foundation Model","created":"2024-10-13","cited_by_count":1,"area":null},{"id":"2412.10946","title":"SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation","created":"2024-12-14","cited_by_count":1,"area":null},{"id":"2506.19658","title":"SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting","created":"2025-06-24","cited_by_count":1,"area":null},{"id":"2508.07797","title":"Power Battery Detection","created":"2025-08-11","cited_by_count":1,"area":null},{"id":"2509.23617","title":"BioVessel-Net and RetinaMix: Unsupervised Retinal Vessel Segmentation from OCTA Images","created":"2025-09-28","cited_by_count":1,"area":null},{"id":"2510.00665","title":"Multi-Domain Brain Vessel Segmentation Through Feature Disentanglement","created":"2025-10-01","cited_by_count":1,"area":null},{"id":"2510.05899","title":"Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning","created":"2025-10-07","cited_by_count":1,"area":null},{"id":"2512.05114","title":"Deep infant brain segmentation from multi-contrast MRI","created":"2025-12-04","cited_by_count":1,"area":null},{"id":"2512.08439","title":"LapFM: A Laparoscopic Segmentation Foundation Model via Hierarchical Concept Evolving Pre-training","created":"2025-12-09","cited_by_count":1,"area":null},{"id":"2604.23187","title":"DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark","created":"2026-04-25","cited_by_count":1,"area":null},{"id":"2605.24410","title":"Advancing Graph Few-Shot Learning via In-Context Learning","created":"2026-05-23","cited_by_count":1,"area":null},{"id":"2606.26711","title":"Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation","created":"2026-06-25","cited_by_count":1,"area":null},{"id":"2311.12539","title":"GMISeg: General Medical Image Segmentation without Re-Training","created":"2023-11-21","cited_by_count":0,"area":null},{"id":"2405.06814","title":"Dual-Task Vision Transformer for Rapid and Accurate Intracerebral Hemorrhage CT Image Classification","created":"2024-05-10","cited_by_count":0,"area":null},{"id":"2411.08537","title":"MLV$^2$-Net: Rater-Based Majority-Label Voting for Consistent Meningeal Lymphatic Vessel Segmentation","created":"2024-11-13","cited_by_count":0,"area":null},{"id":"2412.19026","title":"Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation","created":"2024-12-26","cited_by_count":0,"area":null},{"id":"2501.02140","title":"Tree-NET: Enhancing 2D Medical Image Segmentation Through Efficient Low-Level Feature Training","created":"2025-01-03","cited_by_count":0,"area":null},{"id":"2506.12747","title":"Unleashing Diffusion and State Space Models for Medical Image Segmentation","created":"2025-06-15","cited_by_count":0,"area":null},{"id":"2508.09196","title":"FIVA: Federated Inverse Variance Averaging for Universal CT Segmentation with Uncertainty Estimation","created":"2025-08-08","cited_by_count":0,"area":null},{"id":"2508.11469","title":"CoFi: A Fast Coarse-to-Fine Few-Shot Pipeline for Glomerular Basement Membrane Segmentation","created":"2025-08-15","cited_by_count":0,"area":null},{"id":"2509.08618","title":"CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging","created":"2025-09-10","cited_by_count":0,"area":null},{"id":"2511.05782","title":"TCSA-UDA: Text-Driven Cross-Semantic Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation","created":"2025-11-08","cited_by_count":0,"area":null},{"id":"2511.21984","title":"PPBoost: Progressive Prompt Boosting for Text-Driven Medical Image Segmentation","created":"2025-11-26","cited_by_count":0,"area":null},{"id":"2512.13534","title":"Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains","created":"2025-12-15","cited_by_count":0,"area":null},{"id":"2603.00459","title":"Explainable Continuous-Time Mask Refinement with Local Self-Similarity Priors for Medical Image Segmentation","created":"2026-02-28","cited_by_count":0,"area":null},{"id":"2603.06167","title":"A Semi-Supervised Framework for Breast Ultrasound Segmentation with Training-Free Pseudo-Label Generation and Label Refinement","created":"2026-03-06","cited_by_count":0,"area":null},{"id":"2603.11659","title":"FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentation","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2604.02564","title":"Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It","created":"2026-04-02","cited_by_count":0,"area":null},{"id":"2605.16903","title":"WOW-Seg: A Word-free Open World Segmentation Model","created":"2026-05-16","cited_by_count":0,"area":null},{"id":"2607.12896","title":"UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation","created":"2026-07-14","cited_by_count":0,"area":null},{"id":"2609.03080","title":"Exemplar: Classical Priors Complement Frozen Features for Few-Shot Microscopy Segmentation at Native Resolution","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.10001","title":"From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysis","created":"2026-09-09","cited_by_count":0,"area":null},{"id":"2609.34235","title":"SegBanana: Steering Unified Multimodal Models into Medical Segmenters","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.38968","title":"Beyond Spatial-Domain Supervision: A Relation Constrained Space for Multi-Modal Image Fusion","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2609.39004","title":"When Integral Meets Decomposition: A Signal-Level Self-Supervised Feature Decompose Paradigm for Multi-Modal Image Fusion","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.06938","title":"UniPro: Unified Multi-Mode Medical Image Segmentation from 2D Images to 3D Volumes via Propagation","created":"2026-10-03","cited_by_count":0,"area":null}],"next_cursor":null}