{"total":39,"rows":[{"id":"2405.06880","title":"EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation","created":"2024-05-11","cited_by_count":64,"area":null},{"id":"2205.04280","title":"TGANet: Text-guided attention for improved polyp segmentation","created":"2022-05-09","cited_by_count":59,"area":null},{"id":"2311.18363","title":"Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation","created":"2023-11-30","cited_by_count":29,"area":null},{"id":"2205.08473","title":"ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation","created":"2022-05-17","cited_by_count":28,"area":null},{"id":"2303.07428","title":"TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing","created":"2023-03-13","cited_by_count":26,"area":null},{"id":"2206.08985","title":"TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation","created":"2022-06-17","cited_by_count":19,"area":null},{"id":"2408.04212","title":"Is SAM 2 Better than SAM in Medical Image Segmentation?","created":"2024-08-08","cited_by_count":16,"area":null},{"id":"2303.06580","title":"Towards General Purpose Medical AI: Continual Learning Medical Foundation Model","created":"2023-03-12","cited_by_count":14,"area":null},{"id":"2410.17241","title":"Frontiers in Intelligent Colonoscopy","created":"2024-10-22","cited_by_count":14,"area":null},{"id":"2203.00129","title":"BlazeNeo: Blazing fast polyp segmentation and neoplasm detection","created":"2022-02-28","cited_by_count":10,"area":null},{"id":"2504.21336","title":"UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation","created":"2025-04-30","cited_by_count":10,"area":null},{"id":"2305.17937","title":"Attention Mechanisms in Medical Image Segmentation: A Survey","created":"2023-05-29","cited_by_count":9,"area":null},{"id":"2409.00045","title":"PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy","created":"2024-08-19","cited_by_count":7,"area":null},{"id":"2412.16078","title":"SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data","created":"2024-12-20","cited_by_count":7,"area":null},{"id":"2210.13595","title":"DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation","created":"2022-10-24","cited_by_count":6,"area":null},{"id":"2212.09263","title":"Focal-UNet: UNet-like Focal Modulation for Medical Image Segmentation","created":"2022-12-19","cited_by_count":6,"area":null},{"id":"2412.13533","title":"Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments","created":"2024-12-18","cited_by_count":6,"area":null},{"id":"2401.11734","title":"Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey","created":"2024-01-22","cited_by_count":5,"area":null},{"id":"2310.05538","title":"M3FPolypSegNet: Segmentation Network with Multi-frequency Feature Fusion for Polyp Localization in Colonoscopy Images","created":"2023-10-09","cited_by_count":3,"area":null},{"id":"2506.00406","title":"iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection","created":"2025-05-31","cited_by_count":3,"area":null},{"id":"2210.04393","title":"LAPFormer: A Light and Accurate Polyp Segmentation Transformer","created":"2022-10-10","cited_by_count":2,"area":null},{"id":"2307.06420","title":"RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation","created":"2023-07-12","cited_by_count":2,"area":null},{"id":"2206.06264","title":"Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network","created":"2022-06-13","cited_by_count":1,"area":null},{"id":"2209.14599","title":"Online pseudo labeling for polyp segmentation with momentum networks","created":"2022-09-29","cited_by_count":1,"area":null},{"id":"2306.02176","title":"TransRUPNet for Improved Polyp Segmentation","created":"2023-06-03","cited_by_count":1,"area":null},{"id":"2310.05446","title":"RetSeg: Retention-based Colorectal Polyps Segmentation Network","created":"2023-10-09","cited_by_count":1,"area":null},{"id":"2511.06769","title":"RRTS Dataset: A Benchmark Colonoscopy Dataset from Resource-Limited Settings for Computer-Aided Diagnosis Research","created":"2025-11-10","cited_by_count":1,"area":null},{"id":"2511.16618","title":"SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking","created":"2025-11-20","cited_by_count":1,"area":null},{"id":"2602.05937","title":"Multi-Scale Global-Instance Prompt Tuning for Continual Test-time Adaptation in Medical Image Segmentation","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2603.11492","title":"SPEGC: Continual Test-Time Adaptation via Semantic-Prompt-Enhanced Graph Clustering for Medical Image Segmentation","created":"2026-03-12","cited_by_count":1,"area":null},{"id":"2308.03709","title":"Prototype Learning for Out-of-Distribution Polyp Segmentation","created":"2023-08-07","cited_by_count":0,"area":null},{"id":"2407.19327","title":"Polyp segmentation in colonoscopy images using DeepLabV3++","created":"2024-07-27","cited_by_count":0,"area":null},{"id":"2409.05875","title":"Transformer-Enhanced Iterative Feedback Mechanism for Polyp Segmentation","created":"2024-08-25","cited_by_count":0,"area":null},{"id":"2410.13472","title":"Day-Night Adaptation: An Innovative Source-free Adaptation Framework for Medical Image Segmentation","created":"2024-10-17","cited_by_count":0,"area":null},{"id":"2507.08357","title":"Cycle Context Verification for In-Context Medical Image Segmentation","created":"2025-07-11","cited_by_count":0,"area":null},{"id":"2602.09587","title":"MieDB-100k: A Comprehensive Dataset for Medical Image Editing","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2604.17455","title":"From Adaptation to Generalization: Adaptive Visual Prompting for Medical Image Segmentation","created":"2026-04-19","cited_by_count":0,"area":null},{"id":"2608.29677","title":"MedSegBenchmarker: A Raw-Count-First Framework for Controlled 2D Medical Image Segmentation Benchmarks","created":"2026-08-30","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}