Background detection affects downstream classification of Camelyon16 whole slide images

Thomas E. Tavolara; M. Khalid Khan Niazi; Metin N. Gurcan

doi:10.1117/12.2653882

6 April 2023 Background detection affects downstream classification of Camelyon16 whole slide images

Thomas E. Tavolara, M. Khalid Khan Niazi, Metin N. Gurcan

Author Affiliations +

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124710O (2023) https://doi.org/10.1117/12.2653882
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Detecting the background is often one of the first tasks when analyzing whole slide images. However, these methods greatly vary and are generally not reported in sufficient detail for subsequent studies to be able to reproduce. Therefore, we sought to determine the effect of varying background detection methods on downstream performance metrics. Specifically, we applied attention-based multiple instance learning, CLAM, and Attention2majority to classify whole slide images from Camelyon16 using four different background detection methods. Our results show that performance metrics (i.e., accuracy and area under the curve - AUC) differ when different background detection methods are utilized. Furthermore, all classification methods perform worse when using hand-drawn tissue annotations for background detection. Finally, we show that depending on the background detection method, the best performing method changes. We conclude that background detection methods must be fully reported so that results can be accurately reproduced and techniques can be fairly compared.

Conference Presentation

Citation Download Citation

Thomas E. Tavolara, M. Khalid Khan Niazi, and Metin N. Gurcan "Background detection affects downstream classification of Camelyon16 whole slide images", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124710O (6 April 2023); https://doi.org/10.1117/12.2653882

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