Our lab is dedicated to researching federated learning for medical image analysis. This repo collects official implementation of our works.

M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models

The official Meta Llama 3 GitHub site

Grok open release

A lightweight CNN-based model for medical image segmentation.

[CVPR 2023] Label-Free Liver Tumor Segmentation

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

Tips for Writing a Research Paper using LaTeX

Implementation of MedSegDiff in Pytorch - SOTA medical segmentation using DDPM and filtering of features in fourier space

Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond

[MICCAI'22] Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift

code for our work: RFNet for Incomplete Multi-modal Brain Tumor Segmentation

MSDESIS: Multi-task stereo disparity estimation and surgical instrument segmentation

An easy-to-use federated learning platform

[TMI 2023] XBound-Former: Toward Cross-scale Boundary Modeling in Transformers

A simplified library for decentralized, privacy preserving machine learning

FedUL: Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients

[MedIA 2021] Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network

[MICCAI 2021] Boundary-aware Transformers for Skin Lesion Segmentation

[AAAI 2022 Oral] Separate Contrastive Learning for Organs-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation

⏰ Agenticly track worldwide conference deadlines (Website, Python Cli, Wechat Applet)

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

[CVPR'21] FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

A state-of-the-art semi-supervised method for image recognition

Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision (CVPR 2020 Oral)