Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images
This addresses the challenge of computer-assisted diagnosis for a common skin cancer, though it appears incremental as it applies existing attention methods to a specific domain problem.
The study tackled the problem of detecting basal cell carcinomas in ultra-high resolution histopathological images with weak labels, and demonstrated that attention-based deep learning models achieved near-perfect classification with an AUC of 0.99.
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.