AI Toolkit for Healthcare Imaging
- healthcare-imaging
- deep-learning
- medical-image-computing
- medical-image-processing
Repository Index
60,121 visible repositories in the index
1–24 of 26 · Most stars
OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages
- nci-itcr
- nci-qin
- quantitative-imaging
- imaging-informatics
Multi-platform, free open source software for visualization and image computing.
- medical-imaging
- vtk
- itk
- python
[Deprecated] Use Cornerstone3D Instead https://cornerstonejs.org/
- javascript
- cornerstone
- dicom
- medical-imaging
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
- medical-imaging
- medical-ai
- deep-learning
- machine-learning
⚡High Performance DICOM Medical Image Parser in Go.
- dicom
- dicom-images
- golang
- golang-library
Simultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.
Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data
A pure JavaScript medical research image viewer.
Vision-Language Pathology Foundation Model - Nature Medicine
- bioimage-analysis
- bioimage-informatics
- conch
- foundation-model
Dicoogle - Open Source PACS
- dicoogle
- dicom
- pacs
- medical-imaging
C library for reading virtual slide images
- pathology
- whole-slide-imaging
- c
Tools for computational pathology
- machine-learning
- digital-pathology
- computational-pathology
- biomedical-image-processing
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.
- artificial-intelligence
- clip
- pathology
- vision-and-language
Kaapana is an open source toolkit for state of the art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. The name Kaapana comes from the Hawaiian word kaʻāpana, meaning "distributor" or "part".
- imaging
- ai
- cloud-computing
- medical-imaging
Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell
- histology
- wsi-images
- pan-cancer-analysis
- genomics
Automated 3D cell detection in very large images
- microscopy
- cell-detection
- deep-learning
- image-analysis
Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk
- cancer-imaging-research
- radiomics
- radiomics-features
- machine-learning
AI-based pathology predicts origins for cancers of unknown primary - Nature
- histology
- pathology
- wsi-images
- histopathology
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
- generative-adversarial-network
- mri-reconstruction
- computer-vision
- deep-learning
Interoperable web-based DICOM slide microscopy viewer and annotation tool
⚡ Open-source software for deep learning-based digital pathology
- deep-learning
- machine-learning
- digital-pathology
- computational-pathology
Axon/Myelin segmentation using Deep Learning
- deep-learning
- machine-learning
- myelin
- axon
Classification and Gradient-based Localization of Chest Radiographs using PyTorch.
- fine-tuning
- vgg16
- resnet18
- densenet121