Stars
The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that sho…
Free-Text Promptable Universal 3D Medical Image Segmentation
Vim-fork focused on extensibility and usability
JetStream2 backed on-demand virtual machines
A distributed, github based platform to share and collaborate on segmentations using 3D Slicer
Official inference repo for FLUX.1 models
A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.
nnInteractive is a framework for 3D interactive segmentation, supporting intuitive prompts like points, scribbles, bounding boxes, and lasso. Trained on 120+ diverse 3D datasets, it sets a new stan…
An educational resource to help anyone learn deep reinforcement learning.
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…
Connected components on discrete and continuous multilabel 3D & 2D images. Handles 26, 18, and 6 connected variants; periodic boundaries (4, 8, & 6)
Library to compute surface distance based performance metrics for segmentation tasks.
The RISE Journal Club aims to create a friendly environment to discuss the latest state-of-the-art papers in the areas of medical image analysis, AI and computer vision. The moderators will briefly…
Segment Anything Model for Medical Image Segmentation: Open-Source Project Summary
Tool for robust segmentation of >100 important anatomical structures in CT and MR images
🤘 awesome-semantic-segmentation
PyTorch implementation of the InfoNCE loss for self-supervised learning.
Machine learning metrics for distributed, scalable PyTorch applications.
Fast and differentiable MS-SSIM and SSIM for pytorch.
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Roughness Index and Roughness Distance for Benchmarking Medical Segmentation
Medical imaging processing for AI applications.
[MICCAI 2019 Young Scientist Award] [MEDIA 2020 Best Paper Award] Models Genesis, one of the first "foundation" models in medical image analysis for multiple downstream tasks