Dermoscopic diagnostic performance of Japanese dermatologists for skin tumors differs by patient origin: A deep learning convolutional neural network closes the gap-Reference-Cited by-同舟云学术

Dermoscopic diagnostic performance of Japanese dermatologists for skin tumors differs by patient origin: A deep learning convolutional neural network closes the gap

Published:2020-10-15 Issue:2 Volume:48 Page:232-236
ISSN:0385-2407
Container-title:The Journal of Dermatology
language:en
Short-container-title:J. Dermatol.

Author:

Minagawa Akane¹^ORCID,Koga Hiroshi¹^ORCID,Sano Tasuku¹,Matsunaga Kazuhisa²,Teshima Yoshihiro²,Hamada Akira²,Houjou Yoshiharu²,Okuyama Ryuhei¹

Affiliation:

1. Department of Dermatology Shinshu University School of Medicine Matsumoto Japan

2. Casio Computer Co., Ltd Tokyo Japan

Funder

Japan Agency for Medical Research and Development

Publisher

Wiley

Subject

Dermatology,General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1346-8138.15640

Reference13 articles.

1. Accuracy of Computer-Aided Diagnosis of Melanoma

2. Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks

3. CodellaNCF GutmanD CelebiMEet al.Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISIB). Available fromhttps://arxiv.org/abs/1710.05006(last accessed June 26 2020).

4. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

5. CombaliaM CodellaNCF RotembergVet al.BCN20000: dermoscopic lesions in the wild. Available from:https://arxiv.org/abs/1908.02288(last accessed June 26 2020).

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