⚡ Open-source software for deep learning-based digital pathology
- deep-learning
- machine-learning
- digital-pathology
- computational-pathology
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⚡ Open-source software for deep learning-based digital pathology
Python package for image-based profiling
A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design
Easy link prediction tool
A tool for cell instance aware segmentation in densely packed 3D volumetric images
The RAW JavaScript library to work with polynomials
Auto-QChem is an automated workflow for the generation and storage of DFT calculations for organic molecules.
Axon/Myelin segmentation using Deep Learning
Clipping and geometric operations for spherical polygons.
A strongly-typed genetic programming framework for Python
Delaunay triangulation for convex polygon
Integrated pipeline for multiplexed image analysis
:spider_web: Compute interesting points, areas, and volumes in simplex meshes of any dimension.
A Swift port of the Cassowary linear constraint solver
Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux
hsolve:电磁仿真大规模非正定厄密特征值求解。Certified shift-invert eigensolver for large sparse Hermitian (indefinite, generalized) eigenproblems from EM simulation: MUMPS LDLᵀ + block Krylov-Schur + Sylvester-inertia completeness certificate.
Derive: Symbolic Math Engine for Derivatives, Integrals, and Vector Operations on Windows
Advanced Windows Mathematics Education Tool with Computational Engine and 2D/3D Visualization
A math and collisions library for Lua.
Toolbox for the analysis of smFISH images.
The Mines Java Toolkit
slick is a simple to use polygon collision library inspired by bump.lua
The Massively Parallel Quantum Chemistry program, MPQC, computes properties of atoms and molecules from first principles using the time independent Schrödinger equation.
Variations on Kirby & Paris' hydra battles and other entertaining math in Coq (collaborative, documented, includes exercises) [maintainer=@Casteran]