The Temple University Hospital Digital Pathology Corpus

D. Houser, G. Shadhin, R. Anstotz, C. Campbell, I. Obeid, J. Picone, T. Farkas, Y. Persidsky, N. Jhala

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Digital pathology is a relatively new field that stands to gain from modern big data and machine learning techniques. In the United States alone, millions of pathology slides are created and interpreted by a human expert each year, suggesting that there is ample data available to support machine learning research. However, the relevant corpora that currently exist contain only hundreds of images, not enough to develop sophisticated deep learning models. This lack of publicly accessible data also hinders the advancement of clinical science. Our digital pathology corpus is an effort to place a large amount of clinical pathology images collected at Temple University Hospital into the public domain to support the development of automatic interpretation technology. The goal of this ambitious project is to create a corpus of 1M images. We have already released 10,000 images from 600 clinical cases. In this paper, we describe the corpus under development and discuss some of the underlying technology that was developed to support this project.

Original languageEnglish (US)
Title of host publication2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538659168
DOIs
StatePublished - Jul 2 2018
Externally publishedYes
Event2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Philadelphia, United States
Duration: Dec 1 2018 → …

Publication series

Name2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings

Conference

Conference2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018
Country/TerritoryUnited States
CityPhiladelphia
Period12/1/18 → …

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Health Informatics

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