{"total":21,"rows":[{"id":"2503.08373","title":"nnInteractive: Redefining 3D Promptable Segmentation","created":"2025-03-11","cited_by_count":41,"area":null},{"id":"2410.09890","title":"Large-Scale 3D Medical Image Pre-training with Geometric Context Priors","created":"2024-10-13","cited_by_count":36,"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":"2503.01835","title":"Primus: Enforcing Attention Usage for 3D Medical Image Segmentation","created":"2025-03-03","cited_by_count":24,"area":null},{"id":"2507.01291","title":"PanTS: The Pancreatic Tumor Segmentation Dataset","created":"2025-07-02","cited_by_count":16,"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":"2507.05582","title":"Learning Segmentation from Radiology Reports","created":"2025-07-08","cited_by_count":11,"area":null},{"id":"2503.13560","title":"MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset","created":"2025-03-17","cited_by_count":6,"area":null},{"id":"2501.03410","title":"Expectation-Maximization as the Engine of Scalable Medical Intelligence","created":"2025-01-06","cited_by_count":5,"area":null},{"id":"2409.08691","title":"Autoregressive Sequence Modeling for 3D Medical Image Representation","created":"2024-09-13","cited_by_count":4,"area":null},{"id":"2605.21906","title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","created":"2026-05-21","cited_by_count":4,"area":null},{"id":"2409.18340","title":"DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learning","created":"2024-09-26","cited_by_count":3,"area":null},{"id":"2512.19091","title":"Auditing Significance, Metric Choice, and Demographic Fairness in Medical AI Challenges","created":"2025-12-22","cited_by_count":2,"area":null},{"id":"2603.27460","title":"Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development","created":"2026-03-29","cited_by_count":2,"area":null},{"id":"2508.13626","title":"State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicability","created":"2025-08-19","cited_by_count":1,"area":null},{"id":"2511.01600","title":"Lite ENSAM: a lightweight cancer segmentation model for 3D Computed Tomography","created":"2025-11-03","cited_by_count":1,"area":null},{"id":"2606.24883","title":"BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases","created":"2026-06-23","cited_by_count":1,"area":null},{"id":"2601.14593","title":"From Volumes to Slices: Computationally Efficient Contrastive Learning for Sequential Abdominal CT Analysis","created":"2026-01-21","cited_by_count":0,"area":null},{"id":"2607.22332","title":"SLIP: Segmentation with Low-latency Interactive Prompting for 3D Medical Images","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2609.36436","title":"Merlin Plus: A Large-Scale, Multi-Cancer, Image-Mask-Report Dataset","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.13915","title":"Where to Look Matters: Rethinking Sub-Volume Sampling in 3D Medical Self-supervised Learning","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}