Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
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Updated
Jul 31, 2024 - Python
Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
A Python toolkit for pathology image analysis algorithms.
A vision-language foundation model for computational pathology - Nature Medicine
AI-based pathology predicts origins for cancers of unknown primary - Nature
The official deployment of the Digital Slide Archive and HistomicsTK.
Fast and scalable search of whole-slide images via self-supervised deep learning - Nature Biomedical Engineering
SeeVIS is a (S)egmentation-fr(ee) (VIS)ualization pipeline for time-lapse image data. It comprises three steps: 1. preprocessing, 2. feature extraction, and 3. an extended version of the space time cube with three novel color mappings adapted to cell colony growth.
ViCAR extracts and employs (Vi)sual (C)ues for an (A)daptive (R)egistration of time-lapse image data recorded in microfluidic devices.
Track single-cells and profile the cell cycle with PCNA images.
CYCASP is a methodology for investigating and understanding (C)olon(Y) growth and (C)ell (A)ttributes at the population level. It couples (SP)atiotemporal changes by relying on two novel data abstractions and a modular algorithm.
cialab/DeepSlides fork to make it work with newer Python libraries.
Analysis of single molecule localization microscopy. 'pointpattern': statistical analysis. 'image': image processing+analysis+classification
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