Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors

Aubreville M, Pan Z, Sievert M, Ammeling J, Ganz J, Oetter N, Stelzle F, Frenken AK, Breininger K, Goncalves M (2024)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Journal

Publisher: Springer Vieweg

Series: Informatik aktuell

City/Town: Wiesbaden

Pages Range: 143-148

Conference Proceedings Title: Bildverarbeitung für die Medizin 2024. BVM 2024

Event location: Erlangen

ISBN: 9783658440367

DOI: 10.1007/978-3-658-44037-4_42

Abstract

The surgical removal of head and neck tumors requires safe margins, which are usually confirmed intraoperatively by means of frozen sections. This method is, in itself, an oversampling procedure, which has a relatively low sensitivity compared to the definitive tissue analysis on paraffin-embedded sections. Confocal laser endomicroscopy (CLE) is an in-vivo imaging technique that has shown its potential in the live optical biopsy of tissue. An automated analysis of this notoriously difficult to interpret modality would help surgeons. However, the images of CLE show a wide variability of patterns, caused both by individual factors but also, and most strongly, by the anatomical structures of the imaged tissue, making it a challenging pattern recognition task. In this work, we evaluate four popular few shot learning (FSL) methods towards their capability of generalizing to unseen anatomical domains in CLE images. We evaluate this on images of sinunasal tumors (SNT) from five patients and on images of the vocal folds (VF) from 11 patients using a cross-validation scheme. The best respective approach reached a median accuracy of 79.6% on the rather homogeneous VF dataset, but only of 61.6% for the highly diverse SNT dataset. Our results indicate that FSL on CLE images is viable, but strongly affected by the number of patients, as well as the diversity of anatomical patterns.

Authors with CRIS profile

Involved external institutions

Technische Hochschule Ingolstadt Germany (DE) Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen Germany (DE) Universitätsklinikum Würzburg Germany (DE)

How to cite

APA:

Aubreville, M., Pan, Z., Sievert, M., Ammeling, J., Ganz, J., Oetter, N.,... Goncalves, M. (2024). Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors. In Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024. BVM 2024 (pp. 143-148). Erlangen, DE: Wiesbaden: Springer Vieweg.

MLA:

Aubreville, Marc, et al. "Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors." Proceedings of the German Conference on Medical Image Computing, BVM 2024, Erlangen Ed. Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Wiesbaden: Springer Vieweg, 2024. 143-148.

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