Recognizing basal cell carcinoma on smartphone‐captured digital histopathology images with a deep neural network-Reference-Cited by-同舟云学术

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Recognizing basal cell carcinoma on smartphone‐captured digital histopathology images with a deep neural network

Published:2019-08-22 Issue:3 Volume:182 Page:754-762
ISSN:0007-0963
Container-title:British Journal of Dermatology
language:en
Short-container-title:Br J Dermatol

Author:

Jiang Y.Q.¹^ORCID,Xiong J.H.²,Li H.Y.³,Yang X.H.¹,Yu W.T.¹,Gao M.¹,Zhao X.²,Ma Y.P.²,Zhang W.¹,Guan Y.F.³,Gu H.⁴,Sun J.F.¹

Affiliation:

1. Department of Dermatopathology Institute of Dermatology, Peking Union Medical College & Chinese Academy of Medical Sciences Nanjing 210042 China

2. Beijing Tulip Partners Technology Co., Ltd Beijing China

3. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI 48109 U.S.A.

4. Department of Physiotherapy Institute of Dermatology, Peking Union Medical College & Chinese Academy of Medical Sciences Nanjing 210042 China

Publisher

Wiley

Subject

Dermatology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/bjd.18026

Reference29 articles.

1. Incidence and prevalence of basal cell carcinoma (BCC) and locally advanced BCC (LABCC) in a large commercially insured population in the United States: A retrospective cohort study

2. Ocular basal cell carcinoma: a brief literature review of clinical diagnosis and treatment

3. Digital Imaging in Pathology: Whole-Slide Imaging and Beyond

4. Histopathological Image Analysis: A Review

5. Comparison of Pathologist-Detected and Automated Computer-Assisted Image Analysis Detected Sentinel Lymph Node Micrometastases in Breast Cancer

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