KimiaNet is a histopathology deep feature extractor trained from scratch with more than 240,000 image patches of 1000×1000 pixels. These images were acquired at 20× magnification through our proposed “high-cellularity mosaic” approach to enable the usage of weak labels of 7,126 FFPE whole slide images, spanning 30 primary diagnoses, obtained from the TCGA repository. January 2021
CLAW Stain NormalizationThis study introduces a novel approach for stain normalization based on learning a mixture of multivariate skew-normal distributions for stain clustering and parameter estimation alongside a stain transformation technique. November 2020
ELP Image Descriptor This code is an approximation for the ELP histogram implemented by Python. Using the Kernel idea has decreased the CPU time considerably. October 2018
KIMIA Path960 Image Dataset (960 patches of size 308×168 from histopathology scans belong to 20 different classes; 48 instances per class). November 2017
Consensus Contouring (500 synthetic prostate images and their ground truth images; every image has 20 contours simulating 20 different users; ideal for experimenting with inter-observer variability). September 2017
Consensus Contouring (Prostate MR images of 15 patients contoured by 5 oncologists; for experimenting with inter-observer variability). September 2017
Radon Barcodes This Matlab function extracts a Radon barcode from an image. September 2015