Abstract
Application of deep neural networks in learning underlying dermoscopic patterns and classifying skin-lesion pathology is crucial. It can help in early diagnosis which can lead to timely therapeutic intervention and efficacy. To establish the clinical applicability of such techniques it is important to delineate each pathology with superior accuracy. However, with innumerable types of skin conditions and supervised closed class classification methods trained on limited classes, applicability into clinical workflow could be unattainable. To mitigate this issue our work considers this as an open-set recognition problem. The technique is divided into two stages, closed-set classification of labelled data and open-set recognition for unknown classes which employs an autoencoder for conditional reconstruction of the input image. We compare our technique to a traditional baseline method and demonstrate on ISIC and Derm7pt data, higher accuracy and sensitivity for known as well as unknown classes. In summary, our open-set recognition method for dermoscopic images illustrates high clinical applicability.
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Budhwant, P., Shinde, S., Ingalhalikar, M. (2020). Open-Set Recognition for Skin Lesions Using Dermoscopic Images. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_62
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