Computational and Integrative Pathology

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

SurvivalNet

Deep learning survival models

SCNN

Survival Convolutional Neural Networks

NuCLS

NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation

HiPS

Histomic Prognostic Signature (HiPS): A population-level computational histologic signature for invasive breast cancer prognosis

HistomicsML2

A tool for training machine-learning models with whole-slide imaging datasets

MuTILs_Panoptic

Amgad M, Salgado R, Cooper LA. A panoptic segmentation approach for tumor-infiltrating lymphocyte assessment: development of the MuTILs model and PanopTILs dataset. medRxiv 2022.01.08.22268814.

HistomicsML

An interactive machine learning framework for large histopathology images.

histolab

Library for Digital Pathology Image Processing

MatDigitalPathology

Matlab functions to support analysis of digital pathology images.

HistomicsTK

A Python library for pathology image analysis algorithms.

HistXtract

A pipeline for extracting nuclear morphometry features from whole-slide images.

MiscSurvivalUtils

Miscellaneous tools for data management and classical survival analysis.

hif2gene

IHC-Analysis

glimr

A simplified wrapper for hyperparameter search with Ray Tune.

large_image_eager

Integration of the eager iterator.

Mask_RCNN

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

pathology-hooknet

RipleyK

Calculation of the Ripley K (spatial statistics) value in python