Abstract
The incidence of severely atypical melanocytic lesions (SAML) has been increasing year after year. Early detection of SAML by skin surveillance followed by biopsy and treatment may improve survival and reduce the burden on health care systems. Discovery radiomics can be used to analyze a variety of quantitative features present in pigmented lesions that determine which lesions demonstrate enough atypical changes to pursue medical attention. This study utilizes a novel deep residual group convolutional radiomic sequencer to assess SAML. The discovery radiomic sequencer was evaluated against over 18,000 dermoscopic images of different atypical nevi to achieve a sensitivity of 90% and specificity of 83%. Furthermore, the radiomic sequences produced using the novel deep residual group convolutional radiomic sequencer are visualized and analyzed via t-SNE analysis.
The authors thank the Natural Sciences and Engineering Research Council of Canada, the Canada Research Chairs Program, and Elucid Labs.
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Neher, H., Arlette, J., Wong, A. (2019). Discovery Radiomics for Detection of Severely Atypical Melanocytic Lesions (SAML) from Skin Imaging via Deep Residual Group Convolutional Radiomic Sequencer. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_26
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