Keywords: Artifacts, Histopathology, Transformers, Robustness, Image Corruption
TL;DR: We stress-test Vision Transformers using 10 common histopathological artifacts and find that they are more robust than Convolutional Neural Networks
Abstract: Artifacts on digitized Whole Slide Images like blur, tissue fold, and foreign particles have been demonstrated to degrade the performance of deep convolutional neural networks (CNNs). For prospective deployment of deep learning models in computational histopathology, it is essential that the models are robust to common artifacts. In this work, we stress test multi-head self-attention based Vision Transformer models using 10 common artifacts and compare the performance to CNNs. We discovered that Transformers are substantially more robust to artifacts in histopathological images.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Application: Histopathology
Secondary Subject Area: Application: Other
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