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

2019; Elsevier BV; Volume: 111; Linguagem: Inglês

10.1016/j.ejca.2019.02.005

1879-0852

Titus J. Brinker, Achim Hekler, Alexander Enk, Joachim Klode, Axel Hauschild, Carola Berking, Bastian Schilling, Sebastian Haferkamp, Dirk Schadendorf, Stefan Fröhling, Jochen Utikal, Christof von Kalle, Wiebke Ludwig‐Peitsch, Judith Sirokay, Lucie Heinzerling, Magarete Albrecht, Katharina Baratella, Lena Bischof, Eleftheria Chorti, Anna Dith, Christina Drusio, Nina Giese, Emmanouil Gratsias, Klaus Griewank, Sandra Hallasch, Zdenka Hanhart, Saskia Herz, Katja Hohaus, Philipp Jansen, Finja Jockenhöfer, Theodora Kanaki, Sarah Knispel, Katja Leonhard, Anna Martaki, Liliana Matei, Johanna Matull, Alexandra Olischewski, Maximilian Petri, Jan-Malte Placke, Simon Raub, Katrin Salva, Swantje Schlott, Elsa Sody, Nadine Steingrube, Ingo Stoffels, Selma Ugurel, Wiebke Sondermann, Anne Zaremba, Christoffer Gebhardt, Nina Booken, Maria Christolouka, Kristina Buder‐Bakhaya, Therezia Bokor‐Billmann, Alexander Enk, Patrick Gholam, Holger Hänßle, Martin Salzmann, Sarah K. Schäfer, Knut Schäkel, Timo Schank, Ann-Sophie Bohne, Sophia Deffaa, Katharina Drerup, Friederike Egberts, Anna-Sophie Erkens, Benjamin Ewald, Sandra Falkvoll, Sascha Gerdes, Viola Harde, Axel Hauschild, Marion Jost, Katja Kosova, Laetitia Messinger, Malte Metzner, Kirsten Morrison, Rogina Motamedi, Anja Pinczker, Anne Rosenthal, Natalie Scheller, Thomas Schwarz, Dora Stölzl, Federieke Thielking, Elena Tomaschewski, Ulrike Wehkamp, Michael Weichenthal, Oliver Wiedow, Claudia Bär, Sophia Bender-Säbelkampf, Marc Horbrügger, Ante Karoglan, Luise Kraas, Jörg Faulhaber, Cyrill Géraud, Ze Guo, Philipp Koch, Miriam Linke, Nolwenn Maurier, Verena Müller, Benjamin Thomas, Jochen Utikal, Ali Saeed M. Alamri, Andrea Baczako, Carola Berking, Matthias Betke, Carolin Haas, Daniela Hartmann, Markus V. Heppt, Katharina Kilian, Sebastian Krammer, Natalie Lidia Lapczynski, Sebastian Mastnik, Suzan Nasifoglu, Cristel Ruini, Elke Sattler, Max Schlaak, Hans Wolff, Birgit Achatz, Astrid Bergbreiter, Konstantin Drexler, Monika Ettinger, Sebastian Haferkamp, Anna Halupczok, Marie Hegemann, Verena Dinauer, Maria Maagk, Marion Mickler, Biance Philipp, Anna Wilm, Constanze Wittmann, Anja Gesierich, Valerie Glutsch, Katrin Kahlert, Andreas Kerstan, Bastian Schilling, Philipp Schrüfer,

Cell Image Analysis Techniques

Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists.We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics.The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%.For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks.