Simple patch-wise transformations serve as a mechanism for slide-level augmentation for multiple instance learning applications

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

doi:10.1117/12.2653878

6 April 2023 Simple patch-wise transformations serve as a mechanism for slide-level augmentation for multiple instance learning applications

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

Author Affiliations +

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124711E (2023) https://doi.org/10.1117/12.2653878
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Data augmentation is a commonly applied method to increase the number of training samples for histopathology applications. However, data augmentation has yet to be explored for multiple instance learning applications in histopathology. Therefore, we developed a basic methodology to increase the number of bags for training in an application of attention-based multiple instance learning to classify whole slide histopathology images from Camelyon16. Our method applies common image transformations to histopathology patches at random to generate any number of bags for each slide. Across a ten-fold cross-validation with several magnitudes of augmentation, 10x augmentation (i.e. 10 augmented bags per slide plus the original bag) resulted in normal/tumor accuracies and AUC of 0.9463±0.0263, 0.7531±0.0755, and 0.8847±0.0241, respectively, which significantly exceeded baseline performance (i.e. no augmentation) of.0.9300±0.0363, 0.6918±0.1070, and 0.8827±0.0549, respectively. Additionally, our results suggest that greater levels of augmentation increase overall performance metrics.

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Thomas E. Tavolara, M. Khalid Khan Niazi, and Metin N. Gurcan "Simple patch-wise transformations serve as a mechanism for slide-level augmentation for multiple instance learning applications", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124711E (6 April 2023); https://doi.org/10.1117/12.2653878

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