The official repository for MedSAM: Segment Anything in Medical Images.

Medical SAM 2: Segment Medical Images As Video Via Segment Anything Model 2

Mamba-UNet Zoo

Code of the paper Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation.

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

[IEEE Transactions on Medical Imaging/TMI] This repository is the official implementation of the paper ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation.

Resources of the paper “Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models”.

Exploring CNN and ViT for Semi-Supervised Medical Image Segmentation

Mirror of Visualization Toolkit repository

Towards Accurate Cardiac MRI Segmentation with Variational Autoencoder-Based Unsupervised Domain Adaptation

PyTorch model for the Generalized Probabilistic U-Net. For more details see: https://www.melba-journal.org/papers/2023:005.html

MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery (CVPR 2023)

[CVPR23] Official Implementation of MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation

Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

SSL and SAM

Official PyTorch implementation of SegFormer

Scribbles or Points-based weakly-supervised learning for medical image segmentation, a strong baseline, and tutorial for research and application.

[CVPR 2023] Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

OpenMMLab Detection Toolbox and Benchmark

Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

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