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Although melanoma occurs more rarely than several other skin cancers,patients' long term survival rate is extremely low if the diagnosis is missed.Diagnosis is complicated by a high discordance rate among pathologists whendistinguishing between melanoma and benign melanocytic lesions. A tool thatallows pathology labs to sort and prioritize melanoma cases in their workflowcould improve turnaround time by prioritizing challenging cases and routingthem directly to the appropriate subspecialist. We present a pathology deeplearning system (PDLS) that performs hierarchical classification of digitizedwhole slide image (WSI) specimens into six classes defined by theirmorphological characteristics, including classification of "MelanocyticSuspect" specimens likely representing melanoma or severe dysplastic nevi. Wetrained the system on 7,685 images from a single lab (the reference lab),including the the largest set of triple-concordant melanocytic specimenscompiled to date, and tested the system on 5,099 images from two distinctvalidation labs. We achieved Area Underneath the ROC Curve (AUC) values of 0.93classifying Melanocytic Suspect specimens on the reference lab, 0.95 on thefirst validation lab, and 0.82 on the second validation lab. We demonstratethat the PDLS is capable of automatically sorting and triaging skin specimenswith high sensitivity to Melanocytic Suspect cases and that a pathologist wouldonly need between 30% and 60% of the caseload to address all melanomaspecimens.

A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth