Unsupervised Representation Learning from Pathology Images with Multi-directional Contrastive Predictive Coding

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

Abstract

Digital pathology tasks have benefited greatly from modern deep learning algorithms. However, their need for large quantities of annotated data has been identified as a key challenge. This need for data can be countered by using unsupervised learning in situations where data are abundant but access to annotations is limited. Feature representations learned from unannotated data using contrastive predictive coding (CPC) have been shown to enable classifiers to obtain state of the art performance from relatively small amounts of annotated computer vision data. We present a modification to the CPC framework for use with digital pathology patches. This is achieved by introducing an alternative mask for building the latent context and using a multi-directional PixelCNN autoregressor. To demonstrate our proposed method we learn feature representations from the Patch Camelyon histology dataset. We show that our proposed modification can yield improved deep classification of histology patches.
Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
Subtitle of host publication2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
PublisherIEEE
Pages1254-1258
Number of pages5
ISBN (Electronic)9781665412469
ISBN (Print)9781665429474
DOIs
Publication statusPublished - 25 May 2021
Event2021 IEEE International Symposium on Biomedical Imaging (ISBI) - Nice, France
Duration: 13 Apr 202116 Apr 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2021-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2021 IEEE International Symposium on Biomedical Imaging (ISBI)
Country/TerritoryFrance
CityNice
Period13/04/2116/04/21

Keywords

  • Digital Pathology
  • Medical Imaging
  • Representation Learning
  • Semi-Supervised Learning

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