Literature DB >> 32931808

Clinically Relevant Vulnerabilities of Deep Machine Learning Systems for Skin Cancer Diagnosis.

Xinyi Du-Harpur1, Callum Arthurs2, Clarisse Ganier2, Rick Woolf3, Zainab Laftah3, Manpreet Lakhan3, Amr Salam3, Bo Wan2, Fiona M Watt4, Nicholas M Luscombe5, Magnus D Lynch6.   

Abstract

Entities:  

Year:  2020        PMID： 32931808      PMCID： PMC7990050          DOI： 10.1016/j.jid.2020.07.034

Source DB:  PubMed          Journal:  J Invest Dermatol        ISSN： 0022-202X            Impact factor:   8.551


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  8 in total

1.  A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.

Authors:  Titus J Brinker; Achim Hekler; Alexander H Enk; Joachim Klode; Axel Hauschild; Carola Berking; Bastian Schilling; Sebastian Haferkamp; Dirk Schadendorf; Stefan Fröhling; Jochen S Utikal; Christof von Kalle
Journal:  Eur J Cancer       Date:  2019-03-08       Impact factor: 9.162

2.  Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm.

Authors:  Seung Seog Han; Myoung Shin Kim; Woohyung Lim; Gyeong Hun Park; Ilwoo Park; Sung Eun Chang
Journal:  J Invest Dermatol       Date:  2018-02-08       Impact factor: 8.551

3.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

Authors:  Varun Gulshan; Lily Peng; Marc Coram; Martin C Stumpe; Derek Wu; Arunachalam Narayanaswamy; Subhashini Venugopalan; Kasumi Widner; Tom Madams; Jorge Cuadros; Ramasamy Kim; Rajiv Raman; Philip C Nelson; Jessica L Mega; Dale R Webster
Journal:  JAMA       Date:  2016-12-13       Impact factor: 56.272

4.  Dermatologist-level classification of skin cancer with deep neural networks.

Authors:  Andre Esteva; Brett Kuprel; Roberto A Novoa; Justin Ko; Susan M Swetter; Helen M Blau; Sebastian Thrun
Journal:  Nature       Date:  2017-01-25       Impact factor: 49.962

5.  Automated Classification of Skin Lesions: From Pixels to Practice.

Authors:  Akhila Narla; Brett Kuprel; Kavita Sarin; Roberto Novoa; Justin Ko
Journal:  J Invest Dermatol       Date:  2018-10       Impact factor: 8.551

6.  Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition.

Authors:  Julia K Winkler; Christine Fink; Ferdinand Toberer; Alexander Enk; Teresa Deinlein; Rainer Hofmann-Wellenhof; Luc Thomas; Aimilios Lallas; Andreas Blum; Wilhelm Stolz; Holger A Haenssle
Journal:  JAMA Dermatol       Date:  2019-10-01       Impact factor: 10.282

7.  Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions.

Authors:  Michael Phillips; Helen Marsden; Wayne Jaffe; Rubeta N Matin; Gorav N Wali; Jack Greenhalgh; Emily McGrath; Rob James; Evmorfia Ladoyanni; Anthony Bewley; Giuseppe Argenziano; Ioulios Palamaras
Journal:  JAMA Netw Open       Date:  2019-10-02

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

Authors:  Philipp Tschandl; Cliff Rosendahl; Harald Kittler
Journal:  Sci Data       Date:  2018-08-14       Impact factor: 6.444
  8 in total
  2 in total

Review 1.  Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review.

Authors:  Roxana Daneshjou; Mary P Smith; Mary D Sun; Veronica Rotemberg; James Zou
Journal:  JAMA Dermatol       Date:  2021-11-01       Impact factor: 11.816

2.  Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges.

Authors:  Keerthi B Harish; W Nicholson Price; Yindalon Aphinyanaphongs
Journal:  JMIR Form Res       Date:  2022-04-11
  2 in total

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