A deep learning algorithm with high sensitivity for the detection of basal cell carcinoma in Mohs micrographic surgery frozen sections-Reference-Cited by-同舟云学术

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A deep learning algorithm with high sensitivity for the detection of basal cell carcinoma in Mohs micrographic surgery frozen sections

Published:2021-11 Issue:5 Volume:85 Page:1285-1286
ISSN:0190-9622
Container-title:Journal of the American Academy of Dermatology
language:en
Short-container-title:Journal of the American Academy of Dermatology

Author:

Campanella Gabriele,Nehal Kishwer S.,Lee Erica H.,Rossi Anthony,Possum Brandon,Manuel Genna,Fuchs Thomas J.,Busam Klaus J.

Funder

National Cancer Institute

National Institutes of Health

Publisher

Elsevier BV

Subject

Dermatology

Reference4 articles.

1. Whole slide imaging equivalency and efficiency study: experience at a large academic center;Hanna;Mod Pathol,2019

2. Validation of a digital pathology system including remote review during the COVID-19 pandemic;Hanna;Mod Pathol,2020

3. A deep learning algorithm to detect the presence of basal cell carcinoma on Mohs micrographic surgery frozen sections;Sohn;J Am Acad Dermatol,2020

4. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images;Campanella;Nat Med,2019

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1. Artificial Intelligence for Mohs and Dermatologic Surgery: A Systematic Review and Meta-Analysis;Dermatologic Surgery;2024-07-11

2. Artificial intelligence for nonmelanoma skin cancer;Clinics in Dermatology;2024-06

3. Generating clinical-grade pathology reports from gigapixel whole slide images with HistoGPT;2024-03-18

4. Development and validation of a deep learning model for improving detection of nonmelanoma skin cancers treated with Mohs micrographic surgery;JAAD International;2024-03

5. Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site;npj Precision Oncology;2024-01-03

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