1.Project-MONAI / MONAI
AI Toolkit for Healthcare Imaging
- healthcare-imaging
- deep-learning
- medical-image-computing
- medical-image-processing
- pytorch
- python3
- +1 more
Ranked by Attention Score: stars gained over the last 30 complete days
21 repositories by Attention Score · Oct 6, 2026 – Nov 4, 2026 (UTC)
AI Toolkit for Healthcare Imaging
Multi-platform, free open source software for visualization and image computing.
OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
Pathology Foundation Model - Nature Medicine
3D medical imaging reconstruction software
⚡High Performance DICOM Medical Image Parser in Go.
Fellow Oak DICOM for .NET, .NET Core, Universal Windows, Android, iOS, Mono and Unity
Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data
Library for multiscale visualization of high-resolution multiplexed bioimaging data on the web. Directly renders Zarr and OME-TIFF.
Simultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.
Vision-Language Pathology Foundation Model - Nature Medicine
Tools for computational pathology
Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.
Clinical Histopathology Imaging Evaluation Foundation Model
Dicoogle - Open Source PACS
Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.
Neural networks toolbox focused on medical image analysis
U-Net Brain Tumor Segmentation