Detection of Basal Cell Carcinoma in Whole Slide Images

Hongyan Xu, Dadong Wang, Arcot Sowmya, Ian Katz

Abstract: Basal cell carcinoma (BCC) is a prevalent and increasingly diagnosed form of skin cancer that can benefit from automated whole slide image (WSI) analysis. However, traditional methods that utilize popular network structures designed for natural images, such as the ImageNet dataset, may result in reduced accuracy due to the significant differences between natural and pathology images. In this paper, we analyze skin cancer images using the optimal network obtained by neural architecture search (NAS) on the skin cancer dataset. Compared with traditional methods, our network is more applicable to the task of skin cancer detection. Furthermore, unlike traditional unilaterally augmented (UA) methods, the proposed supernet Skin-Cancer net (SC-net) considers the fairness of training and alleviates the effects of evaluation bias. We use the SC-net to fairly treat all the architectures in the search space and leveraged evolutionary search to obtain the optimal architecture for a skin cancer dataset. Our experiments involve 277,000 patches split from 194 slides. Under the same FLOPs budget (4.1G), our searched ResNet50 model achieves 96.2% accuracy and 96.5% area under the ROC curve (AUC), which are 4.8% and 4.7% higher than those with the baseline settings, respectively.