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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 thatprovides potential concordance information to healthcare providers could helpinform diagnostic, prognostic, and therapeutic decision-making for challengingmelanoma cases. We present a melanoma concordance regression deep learningmodel capable of predicting the concordance rate of invasive melanoma ormelanoma in-situ from digitized Whole Slide Images (WSIs). The salient featurescorresponding to melanoma concordance were learned in a self-supervised mannerwith the contrastive learning method, SimCLR. We trained a SimCLR featureextractor with 83,356 WSI tiles randomly sampled from 10,895 specimensoriginating from four distinct pathology labs. We trained a separate melanomaconcordance regression model on 990 specimens with available concordance groundtruth annotations from three pathology labs and tested the model on 211specimens. We achieved a Root Mean Squared Error (RMSE) of 0.28 +/- 0.01 on thetest set. We also investigated the performance of using the predictedconcordance rate as a malignancy classifier, and achieved a precision andrecall of 0.85 +/- 0.05 and 0.61 +/- 0.06, respectively, on the test set. Theseresults are an important first step for building an artificial intelligence(AI) system capable of predicting the results of consulting a panel of expertsand delivering a score based on the degree to which the experts would agree ona particular diagnosis. Such a system could be used to suggest additionaltesting or other action such as ordering additional stains or genetic tests.

Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression