Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined nevi and melanomas

Melanoma and Skin Cancer

Melanoma recognition by a deep learning convolutional neural network-Performance in different melanoma subtypes and localisations

Eur J Cancer.

Winkler JK, Sies K, Fink C, Toberer F, Enk A, Deinlein T, Hofmann-Wellenhof R, Thomas L, Lallas A, Blum A, Stolz W, Abassi MS, Fuchs T, Rosenberger A, Haenssle HA

doi: 10.1016/j.ejca.2019.11.020.

Melanoma and Skin Cancer

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

JAMA Dermatol.

Tschandl P, Rosendahl C, Akay BN, Argenziano G, Blum A, Braun RP, Cabo H, Gourhant JY, Kreusch J, Lallas A, Lapins J, Marghoob A, Menzies S, Neuber NM, Paoli J, Rabinovitz HS, Rinner C, Scope A, Soyer HP, Sinz C, Thomas L, Zalaudek I, Kittler H

doi: 10.1001/jamadermatol.

Melanoma and Skin Cancer

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

Eur J Cancer.

Maron RC, Weichenthal M, Utikal JS, Hekler A, Berking C, Hauschild A, Enk AH, Haferkamp S, Klode J, Schadendorf D, Jansen P, Holland-Letz T, Schilling B, von Kalle C, Fröhling S, Gaiser MR, Hartmann D, Gesierich A, Kähler KC, Wehkamp U, Karoglan A, Bär C, Brinker TJ, Collabrators.

doi: 10.1016/j.ejca.2019.06.013.

Melanoma and Skin Cancer

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

Ann Oncol.

Haenssle HA, Fink C, Toberer F, Winkler J, Stolz W, Deinlein T, Hofmann-Wellenhof R, Lallas A, Emmert S, Buhl T, Zutt M, Blum A, Abassi MS, Thomas L, Tromme I, Tschandl P, Enk A, Rosenberger A, Reader Study Level I and Level II Groups.

doi: 10.1016/j.annonc.2019.10.013.

Melanoma and Skin Cancer

Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma

JAMA

AlDubayan SH, Conway JR, Camp SY, Witkowski L, Kofman E, Reardon B, Han S, Moore N, Elmarakeby H, Salari K, Choudhry H, Al-Rubaish AM, Al-Sulaiman AA, Al-Ali AK, Taylor-Weiner A, Van Allen EM

doi: 10.1001/jama.2020.20457

Melanoma and Skin Cancer

A comparative dermoscopic and reflectance confocal microscopy study of nevi and melanoma with negative pigment network

J Eur Acad Dermatol Venereol.

Farnetani F, Scope A, Mazzoni L, Mandel VD, Manfredini M, Magi S, Vaschieri C, Kaleci S, Longo C, Ciardo S, Stanganelli I, Pellacani G

doi: 10.1111/jdv.15784.

Melanoma and Skin Cancer

Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer

JAMA Dermatol.

Wang HH, Wang YH, Liang CW, Li YC

doi: 10.1001/jamadermatol.2019.2335.

Rheumatoid Arthritis

Applying cascaded convolutional neural network design further enhances automatic scoring of arthritis disease activity on ultrasound images from rheumatoid arthritis patients

Ann Rheum Dis.

Christensen ABH, Just SA, Andersen JKH, Savarimuthu TR

doi: 10.1136/annrheumdis-2019-216636.

Melanoma and Skin Cancer

Dermoscopy Improves the Diagnostic Accuracy of Melanomas Clinically Resembling Seborrheic Keratosis: Cross-Sectional Study of the Ability to Detect Seborrheic Keratosis-Like Melanomas by a Group of Dermatologists with Varying Degrees of Experience

Dermatology.

Carrera C, Segura S, Aguilera P, Takigami CM, Gomes A, Barreiro A, Scalvenzi M, Longo C, Cavicchini S, Thomas L, Malvehy J, Puig S, Zalaudek I

doi: 10.1159/000486851.

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