Fully Convolutional Network for Melanoma Diagnostics

Understand

This work seeks to determine how modern machine learning techniques may be applied to the previously unexplored topic of melanoma diagnostics using digital pathology.

• We curated a new dataset of 50 patient cases of cutaneous melanoma using digital pathology.
• We provide gold standard annotations for three tissue types (tumour, epidermis, and dermis) which are important for the prognostic measurements known as Breslow thickness and Clark level.
• Then, we devised a novel multi-stride fully convolutional network (FCN) architecture that outperformed other networks trained and evaluated using the same data according to standard metrics.