Deep Learning Datasets

This page is a collection of some of my open-sourced deep learning work’s supplemental materials (i.e., tutorials  / code / datasets from papers)

1. Online supplemental material of “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases”.

The tutorials for each use case are presented below with data:

Use Case Blog Data
Nuclei Segmentation Tutorial Data (1.5G)
Epithelium Segmentation Tutorial Data (336M)
Tubule Segmentation Tutorial Data (90M)
Lymphocyte Detection Tutorial Data (6.3M)
Mitosis Detection Tutorial Data (3.3G)
Invasive Ductal Carcinoma Identification Tutorial Data (1.6G)
Lymphoma Sub-type Classification Tutorial Data (1.4G)

Please note that there has been an update to the overall tutorial pipeline, which is discussed in full here.

2. Tutorial “A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images” (paper)