Clinically-relevant vulnerabilities of deep machine learning systems for skin cancer diagnosis.

Du-Harpur, Xinyi; Arthurs, Callum; Ganier, Clarisse; Woolf, Rick; Laftah, Zainab; Lakhan, Manpreet; Salam, Amr; Wan, Bo; Watt, Fiona M; Luscombe, Nicholas M; Lynch, Magnus D

1-s2.0-S0022202X2032056X-main.pdf (981.3 kB)

Clinically-relevant vulnerabilities of deep machine learning systems for skin cancer diagnosis.

journal contribution

posted on 2021-03-30, 12:28 authored by Xinyi Du-Harpur, Callum Arthurs, Clarisse Ganier, Rick Woolf, Zainab Laftah, Manpreet Lakhan, Amr Salam, Bo Wan, Fiona M Watt, Nicholas M Luscombe, Magnus D Lynch

No description supplied

Funding

Crick (Grant ID: 10110, Grant title: Luscombe FC001110) Cancer Research UK (Grant title: CRUK Studentship - Du)

History

Publisher DOI

https://doi.org/10.1016/j.jid.2020.07.034

Usage metrics

The Francis Crick Institute

Keywords

Bioinformatics Imaging Melanoma Methods/Tools/Techniques Luscombe FC001110 1103 Clinical Sciences 1112 Oncology and Carcinogenesis Dermatology & Venereal Diseases

Licence

Copyright Undetermined

Clinically-relevant vulnerabilities of deep machine learning systems for skin cancer diagnosis.

Funding

Crick (Grant ID: 10110, Grant title: Luscombe FC001110) Cancer Research UK (Grant title: CRUK Studentship - Du)

History

Publisher DOI

Usage metrics

Categories

Keywords

Licence

Exports