Open-Set Recognition for Skin Lesions Using Dermoscopic Images

Autor:	Sumeet Shinde, Madhura Ingalhalikar, Pranav Budhwant
Rok vydání:	2020
Předmět:	Workflow business.industry Computer science Medical imaging Open set Deep neural networks Classification methods Pattern recognition Artificial intelligence Sensitivity (control systems) business Skin lesion Autoencoder
Zdroj:	Machine Learning in Medical Imaging ISBN: 9783030598600 MLMI@MICCAI
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f9a7c576758c261eea7363e1bb0e498c https://doi.org/10.1007/978-3-030-59861-7_62 Zobrazit plný text záznamu