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BY 4.0 license Open Access Published by De Gruyter September 2, 2022

Robust Colon Tissue Cartography with Semi-Supervision

  • Jakob Dexl , Michaela Benz , Petr Kuritcyn , Thomas Wittenberg EMAIL logo , Volker Bruns , Carol Geppert , Arndt Hartmann , Bernd Bischl and Jann Goschenhofer
From the journal Current Directions in Biomedical Engineering
https://doi.org/10.1515/cdbme-2022-1088
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Abstract

We explore the task of tissue classification for colon cancer histology in a low label regime comparing a semi-supervised and a supervised learning strategy in a series of experiments. Further, we investigate the model robustness w.r.t. distribution shifts in the unlabeled data and domain shifts across different scanners to prove their practicality in a histology context. By utilizing unlabeled data in addition to nl = 1000 labeled tiles per class, we yield a substantial increase in accuracy from 89.9% to 91.4%.

Keywords: Computational Pathology; Semi-Supervised Learning; Colon Cancer; Model Robustness
Published Online: 2022-09-02

© 2022 The Author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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From the journal
Current Directions in Biomedical Engineering
Current Directions in Biomedical Engineering
Volume 8 Issue 2

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