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)