Robust Colon Tissue Cartography with Semi-Supervision

Dexl J, Benz M, Kuritcyn P, Wittenberg T, Bruns V, Geppert CI, Hartmann A, Bischl B, Goschenhofer J (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Current Directions in Biomedical Engineering De Gruyter

Book Volume: 8

Pages Range: 344-347

Journal Issue: 2

DOI: 10.1515/cdbme-2022-1088

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%.

Authors with CRIS profile

Thomas Wittenberg
Carol-Immanuel Geppert Institute of Pathology Arndt Hartmann Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE) Ludwig-Maximilians-Universität (LMU) DE Germany (DE)

How to cite

APA:

Dexl, J., Benz, M., Kuritcyn, P., Wittenberg, T., Bruns, V., Geppert, C.-I.,... Goschenhofer, J. (2022). Robust Colon Tissue Cartography with Semi-Supervision. Current Directions in Biomedical Engineering, 8(2), 344-347. https://doi.org/10.1515/cdbme-2022-1088

MLA:

Dexl, Jakob, et al. "Robust Colon Tissue Cartography with Semi-Supervision." Current Directions in Biomedical Engineering 8.2 (2022): 344-347.

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