AI Toolkit for Healthcare Imaging
- healthcare-imaging
- deep-learning
- medical-image-computing
- medical-image-processing
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AI Toolkit for Healthcare Imaging
OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages
Multi-platform, free open source software for visualization and image computing.
3D medical imaging reconstruction software
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.
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.
Library for multiscale visualization of high-resolution multiplexed bioimaging data on the web. Directly renders Zarr and OME-TIFF.
Automated 3D cell detection in very large images
Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk
AI-based pathology predicts origins for cancers of unknown primary - Nature
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
Interoperable web-based DICOM slide microscopy viewer and annotation tool
Axon/Myelin segmentation using Deep Learning
Detecting cancers on mobile phones
Minerva Story is an image viewer that is designed specifically to make it easy for non-expert users to interact with complex tissue images.
Functions for the input/output and visualization of medical imaging data that follow either the ANALYZE, NIfTI or AFNI formats.
a collection of small utlities useful for neuroimaging research