Abstract
Skin conditions represent an enormous health care burden worldwide, and as datasets of skin images grow, there is continued interest in computerized approaches to analyze skin images. In order to explore and gain insights into datasets of skin images, we propose a graph based approach to visualize a progression of similar skin images between pairs of images. In our graph, a node represents both a clinical and dermoscopic image of the same lesion, and an edge between nodes captures the visual dissimilarity between lesions, where dissimilarity is computed by comparing the image responses of a pretrained convolutional neural network. We compute the geodesic/shortest path between nodes to determine a path of progressively visually similar skin lesions. To quantitatively evaluate the quality of the returned path, we propose metrics to measure the number of transitions with respect to the lesion diagnosis, and the progression with respect to the clinical 7-point checklist. Compared to baseline experiments, our approach shows improvements to the quality of the returned paths.
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Acknowledgments
Thanks to the Natural Sciences and Engineering Research Council (NSERC) of Canada for funding and to the NVIDIA Corporation for the donation of a Titan X GPU used in this research. Thanks to Sara Daneshvar for preparing the data used in this work.
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Kawahara, J., Moriarty, K.P., Hamarneh, G. (2017). Graph Geodesics to Find Progressively Similar Skin Lesion Images. In: Cardoso, M., et al. Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics. GRAIL MICGen MFCA 2017 2017 2017. Lecture Notes in Computer Science(), vol 10551. Springer, Cham. https://doi.org/10.1007/978-3-319-67675-3_4
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DOI: https://doi.org/10.1007/978-3-319-67675-3_4
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