Clinical skin lesion diagnosis using representations inspired by dermatologist criteria

Yang, Jufeng, Sun, Xiaoxiao, Liang, Jie and Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 2018. Clinical skin lesion diagnosis using representations inspired by dermatologist criteria. Presented at: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, USA, 18-22 June 2018. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, pp. 1258-1266. 10.1109/CVPR.2018.00137

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Abstract

The skin is the largest organ in human body. Around 30%-70% of individuals worldwide have skin related health problems, for whom effective and efficient diagnosis is necessary. Recently, computer aided diagnosis (CAD) systems have been successfully applied to the recognition of skin cancers in dermatoscopic images. However, little work has concentrated on the commonly encountered skin diseases in clinical images captured by easily-accessed cameras or mobile phones. Meanwhile, for a CAD system, the representations of skin lesions are required to be understandable for dermatologists so that the predictions are convincing. To address this problem, we present effective representations inspired by the accepted dermatological criteria for diagnosing clinical skin lesions. We demonstrate that the dermatological criteria are highly correlated with measurable visual components. Accordingly, we design six medical representations considering different criteria for the recognition of skin lesions, and construct a diagnosis system for clinical skin disease images. Experimental results show that the proposed medical representations can not only capture the manifestations of skin lesions effectively, and consistently with the dermatological criteria, but also improve the prediction performance with respect to the state-of-the-art methods based on uninterpretable features.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	9781538664209
ISSN:	2575-7075
Date of First Compliant Deposit:	12 November 2020
Date of Acceptance:	1 May 2018
Last Modified:	09 Nov 2022 09:37
URI:	https://orca.cardiff.ac.uk/id/eprint/136311

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