Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities

Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions

(2020) H.A. Haenssle et al.   ANNALS OF ONCOLOGY

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

(2019) Titus J. Brinker et al.   EUROPEAN JOURNAL OF CANCER

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

(2019) Titus J. Brinker et al.   EUROPEAN JOURNAL OF CANCER

Pathologist-level classification of histopathological melanoma images with deep neural networks

(2019) Achim Hekler et al.   EUROPEAN JOURNAL OF CANCER

Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study

(2019) Philipp Tschandl et al.   LANCET ONCOLOGY

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

(2019) Roman C. Maron et al.   EUROPEAN JOURNAL OF CANCER

Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

(2018) H A Haenssle et al.   ANNALS OF ONCOLOGY

Deep learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumor diagnosis

(2018) Y. Fujisawa et al.   BRITISH JOURNAL OF DERMATOLOGY

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

(2018) Seung Seog Han et al.   JOURNAL OF INVESTIGATIVE DERMATOLOGY

Opportunities and obstacles for deep learning in biology and medicine

(2018) Travers Ching et al.   Journal of the Royal Society Interface

Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network

(2018) Seung Seog Han et al.   PLoS One

7-Point Checklist and Skin Lesion Classification using Multi-Task Multi-Modal Neural Nets

(2018) Jeremy Kawahara et al.   IEEE Journal of Biomedical and Health Informatics

Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

(2018) Freddie Bray et al.   CA-A CANCER JOURNAL FOR CLINICIANS

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

(2018) Philipp Tschandl et al.   Scientific Data

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks

(2018) Philipp Tschandl et al.   JAMA Dermatology

Deep learning ensembles for melanoma recognition in dermoscopy images

(2017) N. C. F. Codella et al.   IBM JOURNAL OF RESEARCH AND DEVELOPMENT

Brain tumor segmentation with Deep Neural Networks

(2017) Mohammad Havaei et al.   MEDICAL IMAGE ANALYSIS

Dermatologist-level classification of skin cancer with deep neural networks

(2017) Andre Esteva et al.   NATURE

MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images

(2015) Ioannis Giotis et al.   EXPERT SYSTEMS WITH APPLICATIONS

Incidence Estimate of Nonmelanoma Skin Cancer (Keratinocyte Carcinomas) in the US Population, 2012

(2015) Howard W. Rogers et al.   JAMA Dermatology

3D whole body scanners revisited

(2013) H.A.M. Daanen et al.   DISPLAYS

A Prospective Study of Mobile Phones for Dermatology in a Clinical Setting

(2013) Jessika Weingast et al.   JOURNAL OF TELEMEDICINE AND TELECARE