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)