Search Results for author:
Found 3 papers, 1 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
