Unsupervised Learning for Image Registration
- image-registration
- image-alignment
- machine-learning
- deep-learning
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Unsupervised Learning for Image Registration
Multi-platform, free open source software for visualization and image computing.
Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
Pathology Foundation Model - Nature Medicine
Vision-Language Pathology Foundation Model - Nature Medicine
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.
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".
A Python interface to access neuroanatomical atlases
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
A foundation model-driven whole slide image-scale cell phenotyping method with QuPath integration Resources
Functions for the input/output and visualization of medical imaging data that follow either the ANALYZE, NIfTI or AFNI formats.