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Learning More for Free-A Multi Task Learning Approach for Improved Pathology Classification in Capsule Endoscopy

Abstract

The progress in Computer Aided Diagnosis (CADx) of Wireless Capsule Endoscopy (WCE) is thwarted by the lack of data. The inadequacy in richly representative healthy and abnormal conditions results in isolated analyses of pathologies, that can not handle realistic multi-pathology scenarios. In this work, we explore how to learn more for free, from limited data through solving a WCE multicentric, multi-pathology classification problem. Learning more implies to learning more than full supervision would allow with the same data. This is done by combining self supervision with full supervision, under multi task learning. Additionally, we draw inspiration from the Human Visual System (HVS) in designing self supervision tasks and investigate if seemingly ineffectual signals within the data itself can be exploited to gain performance, if so, which signals would be better than others. Further, we present our analysis of the high level features as a stepping stone towards more robust multi-pathology CADx in WCE. Code accompanying this work will be made available on github.

Category

Academic chapter

Language

English

Author(s)

Affiliation

  • SINTEF Digital / Smart Sensors and Microsystems
  • University of Oslo
  • Norwegian University of Science and Technology
  • Innlandet Hospital Trust

Year

2021

Publisher

Springer

Book

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part VII

ISBN

9783030872342

Page(s)

3 - 13

View this publication at Norwegian Research Information Repository