Data Sets and Source Codes
- KIMIA Path24C Image Dataset (24 scans, almost 30,000 images of size 1000×1000). February 2021
- 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
- Representation Learning of Histopathology Images using Graph Neural Networks Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology. April 2020
- A Comparative Study of U-Net Topologies for Background Removal in Histopathology Images This paper compares the performance of different U-Net architectures for tissue segmentation of histopathology whole slide images by variating their backbones. June 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
