Outlier detection in dermatology: Performance of different convolutional neural networks for binary classification of inflammatory skin diseases

Artificial intelligence (AI) and convolutional neural networks (CNNs) represent rising trends in modern medicine. However, comprehensive data on the performance of AI practices in clinical dermatologic images are non‐existent. Furthermore, the role of professional data selection for training remains unknown.

[1]  Jakob Nikolas Kather,et al.  Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours. , 2021, European journal of cancer.

[2]  I. Tromme,et al.  Training general practitioners in melanoma diagnosis: a scoping review of the literature , 2021, BMJ Open.

[3]  Nhat Anh Cao,et al.  Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models , 2021, npj Digital Medicine.

[4]  R. Socher,et al.  Deep learning-enabled medical computer vision , 2021, npj Digital Medicine.

[5]  M. S. Hossain,et al.  Convolutional neural network-based models for diagnosis of breast cancer , 2020, Neural Computing and Applications.

[6]  Roman C. Maron,et al.  Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study , 2020, Journal of medical Internet research.

[7]  Marc Pouly,et al.  Künstliche Intelligenz in der Bildanalyse – Grundlagen und neue Entwicklungen , 2020, Der Hautarzt.

[8]  L. V. Maul,et al.  Stellenwert der künstlichen Intelligenz zur Ausbreitungsdiagnostik und Verlaufsbeurteilung von Dermatosen , 2020, Der Hautarzt.

[9]  R. Maron,et al.  Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective , 2020, Frontiers in Medicine.

[10]  E. Kontopantelis,et al.  National, regional, and worldwide epidemiology of psoriasis: systematic analysis and modelling study , 2020, BMJ.

[11]  Å. Svensson,et al.  Prevalence and Incidence of Atopic Dermatitis: A Systematic Review , 2020, Acta dermato-venereologica.

[12]  Bo Zhang,et al.  A deep learning, image based approach for automated diagnosis for inflammatory skin diseases , 2020, Annals of translational medicine.

[13]  X Du-Harpur,et al.  What is AI? Applications of artificial intelligence to dermatology† , 2020, The British journal of dermatology.

[14]  Rainer Hofmann-Wellenhof,et al.  A deep learning system for differential diagnosis of skin diseases , 2019, Nature Medicine.

[15]  Tim Holland-Letz,et al.  Superior skin cancer classification by the combination of human and artificial intelligence. , 2019, European journal of cancer.

[16]  T. Nijsten,et al.  Insight into the management of actinic keratosis: a qualitative interview study among general practitioners and dermatologists† , 2019, The British journal of dermatology.

[17]  Achim Hekler,et al.  Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review , 2018, Journal of medical Internet research.

[18]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[19]  LinLin Shen,et al.  Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network , 2017, Sensors.

[20]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[21]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Xavier Giro-i-Nieto,et al.  Skin lesion classification from dermoscopic images using deep learning techniques , 2017, 2017 13th IASTED International Conference on Biomedical Engineering (BioMed).

[23]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[26]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[27]  R. Hay,et al.  The global burden of skin disease in 2010: an analysis of the prevalence and impact of skin conditions. , 2014, The Journal of investigative dermatology.

[28]  Sarah Irwin,et al.  Qualitative secondary data analysis: Ethics, epistemology and context , 2013 .

[29]  M. Augustin,et al.  Prevalence of skin lesions and need for treatment in a cohort of 90 880 workers , 2011, The British journal of dermatology.

[30]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[31]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .