Deep Learning Segmentation of Invasive Melanoma

Melanoma is the deadliest skin cancer with the fastest rising incidence rate in the United States. The most important predictor of melanoma patient survival is the volume of invasive tumor at the initial biopsy. The appearance of in-situ melanoma in epidermis and invasive melanoma in dermis, which invades the underlying soft tissue and drives mortality, is often visually similar. We propose a novel two-stage method to segment invasive melanoma. The first stage computes two segmentation maps, one for tumor vs non-tumor and one for dermis vs epidermis. These two segmentation prediction maps of tumor and epidermis from the first stage combine to yield invasive melanoma predictions. Our method utilizes multiple resolutions and downscaling to increase information passed to the model and to improve model accuracy. Using an HRNet+OCR model for both epidermis and melanoma segmentation in our proposed two-stage system results in a marked improvement of F1 score (mIoU) to 0.44 (0.64) as compared to the current state-of-the-art of 0.14 (0.53).