DebeshJha/TransNetR
Official implementation of TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing (MIDL 2022)
This project helps medical professionals and researchers accurately identify polyps in colonoscopy images. It takes raw medical images and outputs precise segmentation masks highlighting the polyps, even when dealing with varied image sources or data not seen during initial training. Medical image analysis specialists and gastroenterologists would find this beneficial for improving diagnostic accuracy.
No commits in the last 6 months.
Use this if you need to precisely segment polyps in biomedical images, especially when dealing with data from diverse sources or settings that might differ from your initial training data.
Not ideal if your primary goal is general object detection or image classification for non-biomedical applications.
Stars
24
Forks
5
Language
Python
License
MIT
Last pushed
Feb 23, 2024
Commits (30d)
0
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