Official repo for Medical Image Segmentation Review: The Success of U-Net

[ICCV 2025] AbdomenAtlas 3.0 (9,262 CT volumes + medical reports). These “superhuman” reports are more accurate, detailed, standardized, and generated faster than traditional human-made reports.

[NeurIPS 2023] AbdomenAtlas 1.0 (5,195 CT volumes + 9 annotated classes)

[ICLR 2024 Oral] Supervised Pre-Trained 3D Models for Medical Image Analysis (9,262 CT volumes + 25 annotated classes)

[NeurIPS 2024] Touchstone - Benchmarking AI on 5,172 o.o.d. CT volumes and 9 anatomical structures

[ICCV 2023] CLIP-Driven Universal Model; Rank first in MSD Competition.

Implemention of the Paper "MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation"

Holistic Adaptation of SAM from 2D to 3D for Promptable Medical Image Segmentation

[ICLR'21] FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

code of DenseVoxNet and 3D-DSN

An unofficial PyTorch implementation of VoxelMorph- An unsupervised 3D deformable image registration method

A fast medical imaging analysis library in Python with algorithms for registration, segmentation, and more.

Thin wrapper around elastix - a toolbox for rigid and nonrigid registration of images

Learning nonlinear operators via DeepONet

Segment Anything in Medical Images

An NVIDIA AI Workbench example project for fine-tuning a Mistral 7B model

Codes and models for Medical Image Analysis (MIA) 2023 paper. Segment Anything Model for Medical Images?.

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.

PyTorch implementation of "Attention Is All You Need" by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin

A curated, but incomplete, list of data-centric AI resources.

Democratizing Reinforcement Learning for LLMs

Unsupervised Learning for Image Registration

f-GANs in an Information Geometric Nutshell

Yes you GAN!

Implementation of Apple's Learning from Simulated and Unsupervised Images through Adversarial Training

GAN讨论群讨论汇总的地方