Stars
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
Tool for robust segmentation of >100 important anatomical structures in CT and MR images
TorchEEG is a library built on PyTorch for EEG signal analysis.
[CIBM'24] Segment Anything Model for Medical Image Segmentation: Open-Source Project Summary
Awesome weakly-supervised image semantic segmentation；scribble，bounding box, point, image tag, and heterogeneous of them. 2016-2025
IJCAI2020 & IJCV2021 🌇 Unsupervised Scene Adaptation with Memory Regularization in vivo
This repo includes ChatGPT prompt curation to use ChatGPT and other LLM tools better.
[MICCAI'23] Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-train
A GPT-4 AI Tutor Prompt for customizable personalized learning experiences.
A lightweight adapter bridges SAM with medical imaging [MedIA]
(ACM TOMM) This is the official code repository for "VM-UNet: Vision Mamba UNet for Medical Image Segmentation".
Official implementation of I2I-Mamba, an image-to-image translation model based on selective state spaces
Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis
[ECCV 2024] ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Medical Image
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
Wavelet Convolutions for Large Receptive Fields. ECCV 2024.
Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
Segment Anything in Medical Images
A curated list of resources for Learning with Noisy Labels
[TPAMI 2025|CVPR 2023] Sparsely Annotated Semantic Segmentation with Adaptive Gaussian Mixtures
Official repository for EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image Segmentation (MICCAI 2024)
Codebase for the paper 'SS SFDA: Self-Supervised Source Free Domain Adaptation for Road Segmentation in Hazardous Environments'
A curated (most recent) list of resources for Learning with Noisy Labels
Implementation of the paper "Weakly-supervised structural component segmentation via scribble annotations"
[CVPR 2023] Hunting Sparsity: Density-Guided Contrastive Learning for Semi-Supervised Semantic Segmentation