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
30 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
Interoperable web-based DICOM slide microscopy viewer and annotation tool
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.
A Python interface to access neuroanatomical atlases
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
Automated 3D cell detection in very large images
Automated 3D brain registration with support for multiple species and atlases.
U-Net Brain Tumor Segmentation
Converts medical images to more displayable formats, e.g. NIfTI to jpg.
Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
Classification and Gradient-based Localization of Chest Radiographs using PyTorch.