idc9

tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

kornia

Geometric Computer Vision Library for Spatial AI

sahi

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

imodels

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

yacs

YACS -- Yet Another Configuration System

asv

Airspeed Velocity: A simple Python benchmarking tool with web-based reporting

pycox

Survival analysis with PyTorch

ASAP

Program for the analysis and visualization of whole-slide images in digital pathology

tyro

Zero-effort CLI interfaces & config objects, from types

SimpleParsing

Simple, Elegant, Typed Argument Parsing with argparse

histolab

Library for Digital Pathology Image Processing

hydra-zen

Create powerful Hydra applications without the yaml files and boilerplate code.

imreg_dft

Image registration using discrete Fourier transform.

aicsimageio

Image Reading, Metadata Conversion, and Image Writing for Microscopy Images in Python

large_image

Python modules to work with large multiresolution images.

yet-another-lightning-hydra-template

Flexible and scalable template based on PyTorch Lightning + Hydra. Efficient workflow and reproducibility for rapid ML experiments.

BCSS

Use this to download all elements of the BCSS dataset described in: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083

FAST-Pathology

⚡ Open-source software for deep learning-based digital pathology

awesome-biological-image-analysis

A curated list of software, tools, pipelines, plugins etc. for image analysis related to biological questions.

ISLP

ISLP package: data and code for labs

cellseg_models.pytorch

Encoder-Decoder Cell and Nuclei segmentation models

veridical-flow

Making it easier to build stable, trustworthy data-science pipelines based on the PCS framework.

seaborn-image

High-level API for attractive and descriptive image visualization in Python

wsic

Whole Slide image (WSI) conversion for brightfield histology images

PathProfiler

Pipeline for computational pathology

CellSeg

napari-wsi

A plugin to read whole slide images within napari.

sciform

A package for formatting numbers into scientific formatted strings.

napari-monailabel

MONAI Label client plugin for napari