Masaya Tanaka1, Atsushi Saito1, Kosuke Shido2, Yasuhiro Fujisawa3, Kenshi Yamasaki2, Manabu Fujimoto4, Kohei Murao5, Youichirou Ninomiya5, Shin'ichi Satoh5, Akinobu Shimizu6. 1. Institute of Engineering, Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan. 2. Department of Dermatology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan. 3. Department of Dermatology, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibanaki, Japan. 4. Department of Dermatology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan. 5. Research Centre for Medical Bigdata, National Institute of Informatics, Chiyoda-ku, Tokyo, Japan. 6. Institute of Engineering, Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan. simiz@cc.tuat.ac.jp.
Abstract
PURPOSE: The purpose of this study was to develop a deep learning-based computer-aided diagnosis system for skin disease classification using photographic images of patients. The targets are 59 skin diseases, including localized and diffuse diseases captured by photographic cameras, resulting in highly diverse images in terms of the appearance of the diseases or photographic conditions. METHODS: ResNet-18 is used as a baseline model for classification and is reinforced by metric learning to boost generalization in classification by avoiding the overfitting of the training data and increasing the reliability of CADx for dermatologists. Patient-wise classification is performed by aggregating the inference vectors of all the input patient images. RESULTS: The experiment using 70,196 images of 13,038 patients demonstrated that classification accuracy was significantly improved by both metric learning and aggregation, resulting in patient accuracies of 0.579 for Top-1, 0.793 for Top-3, and 0.863 for Top-5. The McNemar test showed that the improvements achieved by the proposed method were statistically significant. CONCLUSION: This study presents a deep learning-based classification of 59 skin diseases using multiple photographic images of a patient. The experimental results demonstrated that the proposed classification reinforced by metric learning and aggregation of multiple input images was effective in the classification of patients with diverse skin diseases and imaging conditions.
PURPOSE: The purpose of this study was to develop a deep learning-based computer-aided diagnosis system for skin disease classification using photographic images of patients. The targets are 59 skin diseases, including localized and diffuse diseases captured by photographic cameras, resulting in highly diverse images in terms of the appearance of the diseases or photographic conditions. METHODS: ResNet-18 is used as a baseline model for classification and is reinforced by metric learning to boost generalization in classification by avoiding the overfitting of the training data and increasing the reliability of CADx for dermatologists. Patient-wise classification is performed by aggregating the inference vectors of all the input patient images. RESULTS: The experiment using 70,196 images of 13,038 patients demonstrated that classification accuracy was significantly improved by both metric learning and aggregation, resulting in patient accuracies of 0.579 for Top-1, 0.793 for Top-3, and 0.863 for Top-5. The McNemar test showed that the improvements achieved by the proposed method were statistically significant. CONCLUSION: This study presents a deep learning-based classification of 59 skin diseases using multiple photographic images of a patient. The experimental results demonstrated that the proposed classification reinforced by metric learning and aggregation of multiple input images was effective in the classification of patients with diverse skin diseases and imaging conditions.
Authors: Y Fujisawa; Y Otomo; Y Ogata; Y Nakamura; R Fujita; Y Ishitsuka; R Watanabe; N Okiyama; K Ohara; M Fujimoto Journal: Br J Dermatol Date: 2018-09-19 Impact factor: 9.302
Authors: Philipp Tschandl; Cliff Rosendahl; Bengu Nisa Akay; Giuseppe Argenziano; Andreas Blum; Ralph P Braun; Horacio Cabo; Jean-Yves Gourhant; Jürgen Kreusch; Aimilios Lallas; Jan Lapins; Ashfaq Marghoob; Scott Menzies; Nina Maria Neuber; John Paoli; Harold S Rabinovitz; Christoph Rinner; Alon Scope; H Peter Soyer; Christoph Sinz; Luc Thomas; Iris Zalaudek; Harald Kittler Journal: JAMA Dermatol Date: 2019-01-01 Impact factor: 10.282