Popular GitHub Repositories for Polyp

Offical Code of MICCAI'24 early accepted paper "LGRNet: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos"

Using DUCK-Net for polyp image segmentation. ( Nature Scientific Reports 2023 )

[MICCAI'22] Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection.

Official implementation of TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing (MIDL 2022)

Offical code for 'Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy' [MICCAI 2020]

[ICRA 2025] Polyp-Gen: Realistic and Diverse Polyp Image Generation for Endoscopic Dataset Expansion

End-to-end MLOps architecture built for polyp segmentation — featuring distributed Ray training, MLflow experiment tracking, and automated CI/CD with Kubeflow Pipelines and KServe (Triton) deployment on Google Kubernetes Engine.

Abdominal Organ Segmentation using Multi Decoder Network (MDNet) [Accepted at ICASSP 2025]

DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation

Our project uses state-of-the-art deep learning techniques to tackle a vital medical task: polyp segmentation from colonoscopy images. We harness the Unet++ architecture and a robust tech stack to precisely detect and isolate polyps, advancing healthcare diagnostics and patient care. ??

[WACV 2025] SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation

NanoNet: Real-Time Polyp Segmentation in Capsule Endoscopy & Colonoscopy [Contains an open-access capsule endoscopy dataset (2021 IEEE CBMS)]

Polyp recognition and segmentation for colonoscopy images using UNet++ model.

Project COLON-X (Shaping the neXt frontier in intelligent COLONoscopy)

This repository is a collection of Python scripts and Jupyter notebooks for understanding the performance improvement in image classification, object detection and instance segmentation with OpenVINO. It also contains reference implementations of dwell time analytics, ALPR and polyp detection.

Polyp segmentation using U-Net in PyTorch. This project utilizes the Kvasir-SEG dataset to train and evaluate a deep learning model for accurate segmentation of polyps in endoscopic images.