8 datasets found

The dataset used in this paper consists of whole slide images from five different medical centers, digitized with three different scanners.

The CAMELYON 17 Challenge dataset consists of whole slide images originating from five different medical centers, digitized with three different scanners.

The dataset used for mitosis detection in breast cancer histology images with deep neural networks.

The dataset used for automatic glandular and tubule region segmentation in histological grading of breast cancer.

The dataset used for tubule segmentation of inhomogeneity images based on convolutional neural networks with fluorescence microscopy correction.

The dataset used for invasive ductal carcinoma detection in whole slide images.

The dataset used for lymphoma classification, IDC detection, nuclei segmentation, epithelium segmentation, tubule segmentation, lymphocyte detection, and mitosis detection.

A custom pipeline (histo-fetch) to efficiently extract random patches and labels from pathology whole slide images (WSIs) for input to a neural network on-the-fly.