Video Polyp Segmentation
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Benchmarks
SUN-SEG-Easy (Unseen)
SUN-SEG-Hard (Unseen)
SUN-SEG-Easy
SUN-SEG-Hard
STARE
Most implemented
U-Net: Convolutional Networks for Biomedical Image Segmentation
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Video Polyp Segmentation: A Deep Learning Perspective
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
Progressively Normalized Self-Attention Network for Video Polyp Segmentation
Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?
Papers
ARTEMIS: Agent-guided Reliability-aware Temporal Mask Evolution for Imperfectly Supervised Video Polyp Segmentation
Imperfectly supervised video polyp segmentation (VPS) aims to learn dense, temporally consistent masks from inexpensive supervision, including weak annotations (points, scribbles) and semi-supervision with few densely la…
Video Polyp SegmentationCMSA-Net: Causal Multi-scale Aggregation with Adaptive Multi-source Reference for Video Polyp Segmentation
Video polyp segmentation (VPS) is an important task in computer-aided colonoscopy, as it helps doctors accurately locate and track polyps during examinations. However, VPS remains challenging because polyps often look si…
Video Polyp SegmentationFreeVPS: Repurposing Training-Free SAM2 for Generalizable Video Polyp Segmentation
Existing video polyp segmentation (VPS) paradigms usually struggle to balance between spatiotemporal modeling and domain generalization, limiting their applicability in real clinical scenarios. To embrace this challenge,…
Video Polyp SegmentationDomain GeneralizationFirst-frame Supervised Video Polyp Segmentation via Propagative and Semantic Dual-teacher Network
Automatic video polyp segmentation plays a critical role in gastrointestinal cancer screening, but the cost of frameby-frame annotations is prohibitively high. While sparse-frame supervised methods have reduced this burd…
BenchmarkingTransfer LearningVideo Polyp SegmentationPolyp-SES: Automatic Polyp Segmentation with Self-Enriched Semantic Model
Automatic polyp segmentation is crucial for effective diagnosis and treatment in colonoscopy images. Traditional methods encounter significant challenges in accurately delineating polyps due to limitations in feature rep…
Instance SegmentationMedical Image SegmentationSegmentationVideo Polyp SegmentationSelf-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model
Early diagnosis and treatment of polyps during colonoscopy are essential for reducing the incidence and mortality of Colorectal Cancer (CRC). However, the variability in polyp characteristics and the presence of artifact…
Medical Image SegmentationPolyp SegmentationSegmentationVideo Polyp Segmentation+2