Explainable convolutional neural networks for assessing head and neck cancer histopathology

Dörrich M, Hecht M, Fietkau R, Hartmann A, Iro H, Gostian AO, Eckstein M, Kist A (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Diagnostic Pathology BioMed Central

Book Volume: 18

Article Number: 121

Journal Issue: 1

DOI: 10.1186/s13000-023-01407-8

Abstract

Purpose: Although neural networks have shown remarkable performance in medical image analysis, their translation into clinical practice remains difficult due to their lack of interpretability. An emerging field that addresses this problem is Explainable AI. Methods: Here, we aimed to investigate the ability of Convolutional Neural Networks (CNNs) to classify head and neck cancer histopathology. To this end, we manually annotated 101 histopathological slides of locally advanced head and neck squamous cell carcinoma. We trained a CNN to classify tumor and non-tumor tissue, and another CNN to semantically segment four classes - tumor, non-tumor, non-specified tissue, and background. We applied Explainable AI techniques, namely Grad-CAM and HR-CAM, to both networks and explored important features that contributed to their decisions. Results: The classification network achieved an accuracy of 89.9% on previously unseen data. Our segmentation network achieved a class-averaged Intersection over Union score of 0.690, and 0.782 for tumor tissue in particular. Explainable AI methods demonstrated that both networks rely on features agreeing with the pathologist’s expert opinion. Conclusion: Our work suggests that CNNs can predict head and neck cancer with high accuracy. Especially if accompanied by visual explanations, CNNs seem promising for assisting pathologists in the assessment of cancer sections.

Authors with CRIS profile

Marion Dörrich Department Artificial Intelligence in Biomedical Engineering Rainer Fietkau Lehrstuhl für Strahlentherapie Arndt Hartmann Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie Heinrich Iro Lehrstuhl für Hals-, Nasen- und Ohrenheilkunde Antoniu-Oreste Gostian Department of Otorhinolaryngology – Head and Neck Surgery Markus Eckstein Institute of Pathology Andreas Kist Juniorprofessur für Artificial Intelligence in Communication Disorders

Involved external institutions

Universitätsklinikum des Saarlandes (UKS) DE Germany (DE)

How to cite

APA:

Dörrich, M., Hecht, M., Fietkau, R., Hartmann, A., Iro, H., Gostian, A.-O.,... Kist, A. (2023). Explainable convolutional neural networks for assessing head and neck cancer histopathology. Diagnostic Pathology, 18(1). https://dx.doi.org/10.1186/s13000-023-01407-8

MLA:

Dörrich, Marion, et al. "Explainable convolutional neural networks for assessing head and neck cancer histopathology." Diagnostic Pathology 18.1 (2023).

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