Dicoogle - Open Source PACS
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Dicoogle - Open Source PACS
C library for reading virtual slide images
A tool for semi-automatic cell type classification
A Python native dynamical system simulation framework in the block diagram paradigm.
Large collection of number systems providing custom arithmetic for mixed-precision algorithm development and optimization for AI, Machine Learning, Computer Vision, Signal Processing, CAE, EDA, control, optimization, estimation, and approximation.
Graph-linked unified embedding for single-cell multi-omics data integration
A blazing fast matrix library for Elixir/Erlang with C implementation using CBLAS.
A Practical and Efficient NCBI Taxonomy Toolkit, also supports creating NCBI-style taxdump files for custom taxonomies like GTDB/ICTV
Dense and sparse linear algebra library for Ruby via SciRuby
Advanced date, time and interval library for Java with sun/moon-astronomy and calendars like Chinese, Coptic, Ethiopian, French Republican, Hebrew, Hijri, Historic Christian, Indian National, Japanese, Julian, Korean, Minguo, Persian, Thai, Vietnamese
Tools for computational pathology
The repository host gives no description.
PyKEP is a scientific library providing basic tools for research in interplanetary trajectory design.
GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations
Iterative Linear Quadratic Regulator with auto-differentiatiable dynamics models
A simple script for fracturing polygons. Also adds nice helper functions for polygons like calculateArea, triangulate, getRandomPointsInPolygon, getBoundingRect)
Universal cheminformatics toolkit, utilities and database search tools
Reference mapping for single-cell genomics
V library to develop Artificial Intelligence and High-Performance Scientific Computations
Python library for multilinear algebra and tensor factorizations
A library designed to ease geographic coordinate format conversions, and determine sun/moon information in C#
Encapsulate measurements and their units in Ruby and Ruby on Rails.
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.
An extension for Inkscape that makes it easier to draw optical diagrams.