PraNet: Parallel Reverse Attention Network for Polyp Segmentation, MICCAI 2020 (Oral). Code using Jittor Framework is available.

[VINT 2026] SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation

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

Official PyTorch implementation of UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation (ACMMM 2021)

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

[WACV 2024] An implementation of MEGANet for polyp segmentation with multi-scale edge-guided attention

Frontiers in Intelligent Colonoscopy [ColonSurvey | ColonINST | ColonGPT]

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

Codes for MICCAI2021 paper "Shallow Attention Network for Polyp Segmentation"

PyTorch implementation of ResUNet++ for Medical Image segmentation

Official implementation of NanoNet: Real-time medical Image segmentation architecture (IEEE CBMS)

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

PraNet-V2: Upgrading PraNet from binary (V1) to multi-class (V2) segmentation . Support both Jittor & PyTorch DL frameworks.

PyTorch implementation of medical semantic segmentations models, e.g. UNet, UNet++, DUCKNet, ResUNet, ResUNet++, and support knowledge distillation, distributed training, Optuna etc.

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

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

Liver segmentation using Deep Learning on LiTS 2017 Dataset

S2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation (MICCAI 2023)

[AAAI 2025] MonoBox: Tightness-free Box-supervised Polyp Segmentation using Monotonicity Constraint

Kvasir-SEG: A Segmented Polyp Dataset

PyTorch implementation of DoubleUNet for medical image segmentation

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

Polyp-SAM++ is the first text-guided polyp-segmentation method using segment anything model (SAM).

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.

Implemented Unet++ models for medical image segmentation to detect and classify colorectal polyps.

[BSPC 2025] BMANet: Boundary-guided multi-level attention network for polyp segmentation in colonoscopy images

Official implementation of DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation (pytorch implementation)

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

The open-source code for the paper, EPPS: Advanced Polyp Segmentation via Edge Information Injection and Selective Feature Decoupling.

PolypSeg+: a Lightweight Context-aware Network for Real-time Polyp Segmentation