Official website for "Video Polyp Segmentation: A Deep Learning Perspective (MIR 2022)"

Review in Deep Learning for Polyp Detection and Classification in Colonoscopy (https://doi.org/10.1016/j.neucom.2020.02.123).

2021-MICCAI-Progressively Normalized Self-Attention Network for Video Polyp Segmentation

1st to MICCAI DigestPath2019 challenge (https://digestpath2019.grand-challenge.org/Home/) on colonoscopy tissue segmentation and classification task. (MICCAI 2019) https://teacher.bupt.edu.cn/zhuchuang/en/index.htm

TGANet: Text-guided attention for improved polyp segmentation [Early Accepted & Student Travel Award at MICCAI 2022]

Computational Endoscopy Platform (advanced deep learning toolset for analyzing endoscopy videos) [MICCAI'22, MICCAI'21, ISBI'21, CVPR'20]

Frontiers in Intelligent Colonoscopy [ColonSurvey | ColonINST | ColonGPT]

Official implementation of ColonSegNet: Real-Time Polyp Segmentation (Used in NVIDIA Clara Holoscan App for Polyp Segmentation)

[MICCAI 2024 Oral] SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentation

TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation

Colonoscopy polyps detection with CNNs

A multi-centre polyp detection and segmentation dataset for generalisability assessment https://www.nature.com/articles/s41597-023-01981-y

Liver segmentation using Deep Learning on LiTS 2017 Dataset

GitHub repository for the Kvasir-instrument dataset

Kvasir-SEG: A Segmented Polyp Dataset

GitHub repository for Medico automatic polyp segmentation challenge

This is a repository for the project Detection of Polyps in Colonoscopy. We implement the pipeline for detecting and segmenting the polyps from the capsule endoscopy video feed.

TransRUPNet for Improved Out-of-Distribution Generalization in Polyp Segmentation (IEEE EMBC)

Polyp segmentation tool utilizing U-Net for accurate medical image analysis, designed to enhance early detection and diagnosis of colorectal cancer. Features a user-friendly Streamlit web app for easy image processing and analysis, leveraging the Kvasir-SEG dataset for improved healthcare outcomes.

This repository offers an implementation of the UNet model tailored for semantic segmentation tasks, focusing on detecting polyps in colonoscopy images. It includes comprehensive training scripts, a configurable UNet architecture with an encoder such as ResNet, and a user-friendly inference script.