Papers Video Polyp Segmentation
“Video Polyp Segmentation” 태그가 달린 논문 28편 · 필터 해제
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+2Diff-VPS: Video Polyp Segmentation via a Multi-task Diffusion Network with Adversarial Temporal Reasoning
Diffusion Probabilistic Models have recently attracted significant attention in the community of computer vision due to their outstanding performance. However, while a substantial amount of diffusion-based research has f…
SegmentationVideo Polyp SegmentationLGRNet: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos
Regular screening and early discovery of uterine fibroid are crucial for preventing potential malignant transformations and ensuring timely, life-saving interventions. To this end, we collect and annotate the first ultra…
SegmentationVideo Polyp SegmentationSALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentation
Colonoscopy videos provide richer information in polyp segmentation for rectal cancer diagnosis. However, the endoscope's fast moving and close-up observing make the current methods suffer from large spatial incoherence …
SegmentationVideo Polyp SegmentationVideo SegmentationVideo Semantic SegmentationSSTFB: Leveraging self-supervised pretext learning and temporal self-attention with feature branching for real-time video polyp segmentation
Polyps are early cancer indicators, so assessing occurrences of polyps and their removal is critical. They are observed through a colonoscopy screening procedure that generates a stream of video frames. Segmenting polyps…
Representation LearningSelf-Supervised LearningVideo Polyp SegmentationMAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer
Accurate segmentation of polyps from colonoscopy videos is of great significance to polyp treatment and early prevention of colorectal cancer. However, it is challenging due to the difficulties associated with modelling …
SegmentationVideo Polyp SegmentationShifting More Attention to Breast Lesion Segmentation in Ultrasound Videos
Breast lesion segmentation in ultrasound (US) videos is essential for diagnosing and treating axillary lymph node metastasis. However, the lack of a well-established and large-scale ultrasound video dataset with high-qua…
Lesion SegmentationSegmentationVideo Polyp SegmentationPolyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?
Meta recently released SAM (Segment Anything Model) which is a general-purpose segmentation model. SAM has shown promising results in a wide variety of segmentation tasks including medical image segmentation. In the fiel…
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+1WeakPolyp: You Only Look Bounding Box for Polyp Segmentation
Limited by expensive pixel-level labels, polyp segmentation models are plagued by data shortage and suffer from impaired generalization. In contrast, polyp bounding box annotations are much cheaper and more accessible. T…
Video Polyp SegmentationRectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation
Noisy label problems are inevitably in existence within medical image segmentation causing severe performance degradation. Previous segmentation methods for noisy label problems only utilize a single image while the pote…
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+3AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder
The recently introduced Segment Anything Model (SAM) combines a clever architecture and large quantities of training data to obtain remarkable image segmentation capabilities. However, it fails to reproduce such results …
Image SegmentationSegmentationSemantic SegmentationVideo Polyp SegmentationVideo Polyp Segmentation: A Deep Learning Perspective
We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era. Over the years, developments in VPS are not moving forward with ease due to the lack of large-scale fine-grained segmentat…
AttributeDeep LearningSegmentationSemantic Segmentation+3The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
Humans can easily segment moving objects without knowing what they are. That objectness could emerge from continuous visual observations motivates us to model grouping and movement concurrently from unlabeled videos. Our…
Contrastive LearningImage SegmentationSegmentationSemantic Segmentation+4Full-Duplex Strategy for Video Object Segmentation
Previous video object segmentation approaches mainly focus on using simplex solutions between appearance and motion, limiting feature collaboration efficiency among and across these two cues. In this work, we study a nov…
ObjectObject DetectionSalient Object DetectionSegmentation+6Shallow Attention Network for Polyp Segmentation
Accurate polyp segmentation is of great importance for colorectal cancer diagnosis. However, even with a powerful deep neural network, there still exists three big challenges that impede the development of polyp segmenta…
SegmentationVideo Polyp Segmentation