Deep Learning for Predicting Metastasis on Melanoma WSIs

• URL: http://arxiv.org/abs/2303.05752v1
• Date: Fri, 10 Mar 2023 07:40:09 GMT
• Title: Deep Learning for Predicting Metastasis on Melanoma WSIs
• Authors: Christopher Andreassen, Saul Fuster, Helga Hardardottir, Emiel A.M. Janssen, Kjersti Engan
• Abstract summary: Northern Europe has the second highest mortality rate of melanoma globally. Melanoma prognosis is based on a pathologist's subjective visual analysis of the patient's tumor. This paper presents a convolutional neural network (CNN) method based on VGG16 to predict melanoma prognosis as the presence of metastasis within five years.
• Score: 1.4724454726700604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Northern Europe has the second highest mortality rate of melanoma globally. In 2020, the mortality rate of melanoma rose to 1.9 per 100 000 habitants. Melanoma prognosis is based on a pathologist's subjective visual analysis of the patient's tumor. This methodology is heavily time-consuming, and the prognosis variability among experts is notable, drastically jeopardizing its reproducibility. Thus, the need for faster and more reproducible methods arises. Machine learning has paved its way into digital pathology, but so far, most contributions are on localization, segmentation, and diagnostics, with little emphasis on prognostics. This paper presents a convolutional neural network (CNN) method based on VGG16 to predict melanoma prognosis as the presence of metastasis within five years. Patches are extracted from regions of interest from Whole Slide Images (WSIs) at different magnification levels used in model training and validation. Results infer that utilizing WSI patches at 20x magnification level has the best performance, with an F1 score of 0.7667 and an AUC of 0.81.

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