Multi-Task and Few-Shot Learning-Based Fully Automatic Deep Learning Platform for Mobile Diagnosis of Skin Diseases-Reference-Cited by-同舟云学术

Multi-Task and Few-Shot Learning-Based Fully Automatic Deep Learning Platform for Mobile Diagnosis of Skin Diseases

Published:2023-01 Issue:1 Volume:27 Page:176-187
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Lee Kyungsu¹^ORCID,Cavalcanti T. C.¹^ORCID,Kim Sewoong¹,Lew Hah Min¹^ORCID,Suh Dae Hun²,Lee Dong Hun²^ORCID,Hwang Jae Youn³^ORCID

Affiliation:

1. Department of Electrical Engineering and Computer Science, DGIST, Daegu, Korea

2. Department of Dermatology, Seoul National University College of Medicine, Institute of Human-Environment Interface Biology, Seoul National University, Seoul, South Korea

3. Department of Electrical Engineering and Computer Science, The Interdisciplinary Studies of Artificial Intelligence, DGIST, Daegu, Korea

Funder

National Research Foundation of Korea

Bio & Medical Technology Development Program

National Research Foundation

Korean Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10006388/09839383.pdf?arnumber=9839383

Reference47 articles.

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2. Learning Deep Features for Discriminative Localization

3. Two-photon fluorescence lifetime imaging of human skin papillary dermis in vivo: Assessment of blood capillaries and structural proteins localization;shirshin;Sci Rep,2017

4. A Survey on Multi-Task Learning

5. EVALUATING FLUORESCENCE ILLUMINATION TECHNIQUES FOR SKIN LESION DIAGNOSIS

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