{"total":44,"rows":[{"id":"2406.10729","title":"A Comprehensive Survey of Foundation Models in Medicine","created":"2024-06-15","cited_by_count":56,"area":null},{"id":"2503.08373","title":"nnInteractive: Redefining 3D Promptable Segmentation","created":"2025-03-11","cited_by_count":41,"area":null},{"id":"2410.23132","title":"Revisiting MAE pre-training for 3D medical image segmentation","created":"2024-10-30","cited_by_count":35,"area":null},{"id":"2306.02416","title":"Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation","created":"2023-06-04","cited_by_count":33,"area":null},{"id":"2405.18356","title":"Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography","created":"2024-05-28","cited_by_count":31,"area":null},{"id":"2411.03670","title":"Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?","created":"2024-11-06","cited_by_count":31,"area":null},{"id":"2312.09576","title":"SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma","created":"2023-12-15","cited_by_count":29,"area":null},{"id":"2412.17041","title":"An OpenMind for 3D medical vision self-supervised learning","created":"2024-12-22","cited_by_count":26,"area":null},{"id":"2503.01835","title":"Primus: Enforcing Attention Usage for 3D Medical Image Segmentation","created":"2025-03-03","cited_by_count":24,"area":null},{"id":"2303.17051","title":"Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation","created":"2023-03-29","cited_by_count":17,"area":null},{"id":"2304.14030","title":"COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training","created":"2023-04-27","cited_by_count":16,"area":null},{"id":"2409.09478","title":"From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging","created":"2024-09-14","cited_by_count":16,"area":null},{"id":"2503.19359","title":"Show and Segment: Universal Medical Image Segmentation via In-Context Learning","created":"2025-03-25","cited_by_count":16,"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":"2502.20985","title":"LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging","created":"2025-02-28","cited_by_count":12,"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":"2407.07557","title":"Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging","created":"2024-07-10","cited_by_count":8,"area":null},{"id":"2410.05905","title":"MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model","created":"2024-10-08","cited_by_count":7,"area":null},{"id":"2406.11636","title":"Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities","created":"2024-06-17","cited_by_count":5,"area":null},{"id":"2407.07488","title":"FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse Labels","created":"2024-07-10","cited_by_count":5,"area":null},{"id":"2512.17774","title":"MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Image Segmentation","created":"2025-12-19","cited_by_count":5,"area":null},{"id":"2411.07885","title":"RadioActive: 3D Radiological Interactive Segmentation Benchmark","created":"2024-11-12","cited_by_count":4,"area":null},{"id":"2411.17213","title":"Scaling nnU-Net for CBCT Segmentation","created":"2024-11-26","cited_by_count":4,"area":null},{"id":"2304.10880","title":"Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation","created":"2023-04-21","cited_by_count":3,"area":null},{"id":"2402.19340","title":"One model to use them all: Training a segmentation model with complementary datasets","created":"2024-02-29","cited_by_count":3,"area":null},{"id":"2605.05775","title":"The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\\unicode{x2013}$ Multitracer Multicenter Generalization","created":"2026-05-07","cited_by_count":3,"area":null},{"id":"2406.00327","title":"Quality Sentinel: Estimating Label Quality and Errors in Medical Segmentation Datasets","created":"2024-06-01","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":"2509.15947","title":"The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection","created":"2025-09-19","cited_by_count":2,"area":null},{"id":"2511.19394","title":"BackSplit: The Importance of Sub-dividing the Background in Biomedical Lesion Segmentation","created":"2025-11-24","cited_by_count":2,"area":null},{"id":"2404.15718","title":"Mitigating False Predictions In Unreasonable Body Regions","created":"2024-04-24","cited_by_count":1,"area":null},{"id":"2406.14365","title":"Mask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge","created":"2024-06-20","cited_by_count":1,"area":null},{"id":"2501.04361","title":"A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data","created":"2025-01-08","cited_by_count":1,"area":null},{"id":"2508.21680","title":"Towards Interactive Lesion Segmentation in Whole-Body PET/CT with Promptable Models","created":"2025-08-29","cited_by_count":1,"area":null},{"id":"2510.23241","title":"Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation","created":"2025-10-27","cited_by_count":1,"area":null},{"id":"2403.06396","title":"A Segmentation Foundation Model for Diverse-type Tumors","created":"2024-03-11","cited_by_count":0,"area":null},{"id":"2504.06741","title":"Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation","created":"2025-04-09","cited_by_count":0,"area":null},{"id":"2509.04732","title":"Exploiting Unlabeled Structures through Task Consistency Training for Versatile Medical Image Segmentation","created":"2025-09-05","cited_by_count":0,"area":null},{"id":"2510.03483","title":"DuPLUS: Dual-Prompt Vision-Language Framework for Universal Medical Image Segmentation and Prognosis","created":"2025-10-03","cited_by_count":0,"area":null},{"id":"2512.15921","title":"In search of truth: Evaluating concordance of AI-based anatomy segmentation models","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2601.14605","title":"U-Harmony: Enhancing Joint Training for Segmentation Models with Universal Harmonization","created":"2026-01-21","cited_by_count":0,"area":null},{"id":"2607.03931","title":"GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for $^{18}$F-FDG-PET/CT","created":"2026-07-04","cited_by_count":0,"area":null},{"id":"2607.05009","title":"ImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2608.13660","title":"What to Preserve, Where to Adapt: A Depth-Wise Analysis of Forgetting in Continual Gynecological Image Segmentation","created":"2026-08-13","cited_by_count":0,"area":null}],"next_cursor":null}