Abstract
Melanoma is the most lethal form of skin cancer in the world. To improve the accuracy of diagnosis, quantitative imaging approaches have been investigated. While most quantitative methods focus on the surface of skin lesions via hand-crafted imaging features, in this work, we take a machine-learning approach where abstract quantitative imaging features are learned to model physiological traits. In doing so, we investigate skin cancer detection via computational modeling of two major physiological features of melanoma namely eumelanin and hemoglobin concentrations from dermal images. This was done via employing a non-linear random forest regression model to leverage the plethora of quantitative features from dermal images to build the model. The proposed method was validated by separability test applied to clinical images. The results showed that the proposed method outperforms state-of-the-art techniques on predicting the concentrations of the skin cancer physiological features in dermal images (i.e., eumelanin and hemoglobin).
A. Wong—This research was undertaken, in part, thanks to funding from the Canada Research Chairs program. The study was also funded by the Natural Sciences and Engineering Research Council of Canada (NSERC).
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Cho, D.S., Khalvati, F., Clausi, D.A., Wong, A. (2017). A Machine Learning-Driven Approach to Computational Physiological Modeling of Skin Cancer. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_10
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