Kimia-5MAG-A Dataset for Learning the Magnification in Histopathology Images

Manit Zaveri, Sobhan Hemati, Sultaan Shah, Savvas Damskinos, H. R. Tizhoosh

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

Abstract

Recent advances in medical imaging have created many possibilities for the exploitation of both microscopic images in digital form and the whole slide images (WSIs) for multiple tasks such as classification, prediction, and retrieval. This is mainly due to annotated datasets available through various research organizations. Magnification level is an important factor as pathologist views the biopsy samples at various magnifications to reach a diagnosis. Whereas WSIs generally do contain the magnification information, microscopic snapshots are often captured without attaching the magnification information. In this paper, we introduce a new dataset, Kimia-5MAG, consisting of 33,345 patches at 5 different magnification classes created from WSIs made publicly available by The Cancer Genome Atlas (TCGA). There exists a large number of microscopic snapshots captured from camera-mounted microscopes but are of little use for automatic processing due to lack of magnification information. One direction to make use of these datasets is learning the magnification level from high resolutions captured WSIs and transferring the knowledge to microscopic snapshots. We investigate combinations of several deep networks and classifiers to predict different magnification levels. The proposed framework achieves 93% classification accuracy. We also analyze the effect of rotation on magnification prediction.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020
EditorsMiltos Alamaniotis, Shimei Pan
PublisherIEEE Computer Society
Pages363-367
Number of pages5
ISBN (Electronic)9781728192284
DOIs
StatePublished - Nov 2020
Event32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020 - Virtual, Baltimore, United States
Duration: Nov 9 2020Nov 11 2020

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2020-November
ISSN (Print)1082-3409

Conference

Conference32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020
Country/TerritoryUnited States
CityVirtual, Baltimore
Period11/9/2011/11/20

Keywords

  • Digital Pathology, Magnification learning, AI in healthcare

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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