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Diagnosing basal cell carcinomas (BCC), one of the most common cutaneousmalignancies in humans, is a task regularly performed by pathologists anddermato-pathologists. Improving histological diagnosis by providing diagnosissuggestions, i.e. computer-assisted diagnoses is actively researched to improvesafety, quality and efficiency. Increasingly, machine learning methods areapplied due to their superior performance. However, typical images obtained byscanning histological sections often have a resolution that is prohibitive forprocessing with current state-of-the-art neural networks. Furthermore, the datapose a problem of weak labels, since only a tiny fraction of the image isindicative of the disease class, whereas a large fraction of the image ishighly similar to the non-disease class. The aim of this study is to evaluatewhether it is possible to detect basal cell carcinomas in histological sectionsusing attention-based deep learning models and to overcome the ultra-highresolution and the weak labels of whole slide images. We demonstrate thatattention-based models can indeed yield almost perfect classificationperformance with an AUC of 0.99.

Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images