Detecting cutaneous basal cell carcinomas in ultra-high resolution and
weakly labelled histopathological images
release_l44lyzsqdzezhas3qrsb5ho63u
by
Susanne Kimeswenger, Elisabeth Rumetshofer, Markus Hofmarcher, Philipp
Tschandl, Harald Kittler, Sepp Hochreiter, Wolfram Hötzenecker, Günter
Klambauer
2019
Abstract
Diagnosing basal cell carcinomas (BCC), one of the most common cutaneous
malignancies in humans, is a task regularly performed by pathologists and
dermato-pathologists. Improving histological diagnosis by providing diagnosis
suggestions, i.e. computer-assisted diagnoses is actively researched to improve
safety, quality and efficiency. Increasingly, machine learning methods are
applied due to their superior performance. However, typical images obtained by
scanning histological sections often have a resolution that is prohibitive for
processing with current state-of-the-art neural networks. Furthermore, the data
pose a problem of weak labels, since only a tiny fraction of the image is
indicative of the disease class, whereas a large fraction of the image is
highly similar to the non-disease class. The aim of this study is to evaluate
whether it is possible to detect basal cell carcinomas in histological sections
using attention-based deep learning models and to overcome the ultra-high
resolution and the weak labels of whole slide images. We demonstrate that
attention-based models can indeed yield almost perfect classification
performance with an AUC of 0.99.
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