{"total":17,"rows":[{"id":"2201.12260","title":"A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis","created":"2022-01-28","cited_by_count":29,"area":null},{"id":"2103.00429","title":"Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models","created":"2021-02-28","cited_by_count":22,"area":null},{"id":"2305.07805","title":"Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy","created":"2023-05-13","cited_by_count":13,"area":null},{"id":"2304.07515","title":"S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences","created":"2023-04-15","cited_by_count":11,"area":null},{"id":"2308.04070","title":"ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data","created":"2023-08-08","cited_by_count":6,"area":null},{"id":"2211.07395","title":"Multi-center anatomical segmentation with heterogeneous labels via landmark-based models","created":"2022-11-14","cited_by_count":5,"area":null},{"id":"2112.12665","title":"Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data","created":"2021-12-23","cited_by_count":4,"area":null},{"id":"2404.17967","title":"SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images","created":"2024-04-27","cited_by_count":3,"area":null},{"id":"2302.03296","title":"Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation","created":"2023-02-07","cited_by_count":2,"area":null},{"id":"2502.07145","title":"Mesh2SSM++: A Probabilistic Framework for Unsupervised Learning of Statistical Shape Model of Anatomies from Surface Meshes","created":"2025-02-11","cited_by_count":2,"area":null},{"id":"2409.03228","title":"Labeled-to-Unlabeled Distribution Alignment for Partially-Supervised Multi-Organ Medical Image Segmentation","created":"2024-09-05","cited_by_count":1,"area":null},{"id":"2602.21179","title":"Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervision","created":"2026-02-24","cited_by_count":1,"area":null},{"id":"2109.02230","title":"Non-Euclidean Analysis of Joint Variations in Multi-Object Shapes","created":"2021-09-06","cited_by_count":0,"area":null},{"id":"2209.02706","title":"Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries","created":"2022-09-06","cited_by_count":0,"area":null},{"id":"2303.01036","title":"Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks","created":"2023-03-02","cited_by_count":0,"area":null},{"id":"2512.17279","title":"Diagnostic Performance of Universal-Learning Ultrasound AI Across Multiple Organs and Tasks: the UUSIC25 Challenge","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2603.15862","title":"Self-supervised Disentanglement of Disease Effects from Aging in 3D Medical Shapes","created":"2026-03-16","cited_by_count":0,"area":null}],"next_cursor":null}