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Found 6 papers, 3 papers with code
UGCANet: A Unified Global Context-Aware Transformer-based Network with Feature Alignment for Endoscopic Image Analysis
The early detection and identification of lesions in the upper gastrointestinal tract and the identification of malignant polyps that may pose a risk of cancer development are critical components of gastrointestinal endoscopy's diagnostic and therapeutic applications.
Ranked #3 on
Medical Image Segmentation
on CVC-ClinicDB
RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation
Automatic and accurate segmentation of colon polyps is essential for early diagnosis of colorectal cancer.
ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation
The encoder is a lightweight architecture based on transformers for modeling global semantic relations at multi scales.
Ranked #7 on
Medical Image Segmentation
on CVC-ClinicDB
BlazeNeo: Blazing fast polyp segmentation and neoplasm detection
In recent years, computer-aided automatic polyp segmentation and neoplasm detection have been an emerging topic in medical image analysis, providing valuable support to colonoscopy procedures.
NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection
Automatic polyp segmentation has proven to be immensely helpful for endoscopy procedures, reducing the missing rate of adenoma detection for endoscopists while increasing efficiency.
AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation
Colorectal cancer is among the most common malignancies and can develop from high-risk colon polyps.
Ranked #25 on
Medical Image Segmentation
on CVC-ClinicDB
