Details

Autor(en) / Beteiligte

• Weichenthal, Michael
• Utikal, Jochen S.
• Hekler, Achim
• Berking, Carola
• Hauschild, Axel
• Enk, Alexander H.
• Haferkamp, Sebastian
• Klode, Joachim
• Schadendorf, Dirk
• Jansen, Philipp
• Holland-Letz, Tim
• Schilling, Bastian
• von Kalle, Christof
• Fröhling, Stefan
• Gaiser, Maria R.
• Hartmann, Daniela
• Gesierich, Anja
• Kähler, Katharina C.
• Wehkamp, Ulrike
• Karoglan, Ante
• Bär, Claudia
• Brinker, Titus J.
• Peitsch, Wiebke K.
• Hoffmann, Friederike
• Becker, Jürgen C.
• Drusio, Christina
• Lodde, Georg
• Sammet, Stefanie
• Sondermann, Wiebke
• Ugurel, Selma
• Enk, Alexander
• Salzmann, Martin
• Schäfer, Sarah
• Schäkel, Knut
• Winkler, Julia
• Wölbing, Priscilla
• Asper, Hiba
• Brown, Victoria
• Burba, Bianca
• Deffaa, Sophia
• Dietrich, Cecilia
• Dietrich, Matthias
• Egberts, Friederike
• Harde, Viola
• Jost, Marion
• Kaeding, Merit
• Kosova, Katharina
• Lischner, Stephan
• Messinger, Anna Laetitia
• Metzner, Malte
• Motamedi, Rogina
• Rosenthal, Ann-Christine
• Seidl, Ulrich
• Stemmermann, Jana
• Torz, Kaspar
• Velez, Juliana Giraldo
• Haiduk, Jennifer
• Alter, Mareike
• Bergenthal, Paul
• Gerlach, Anne
• Holtorf, Christian
• Kindermann, Sophie
• Kraas, Luise
• Felcht, Moritz
• Kurzen, Hjalmar
• Leibing, Thomas
• Müller, Verena
• Reinhard, Raphael R.
• Utikal, Jochen
• Winter, Franziska
• Eicher, Laurie
• Heppt, Markus
• Kilian, Katharina
• Krammer, Sebastian
• Lill, Diana
• Niesert, Anne-Charlotte
• Sattler, Elke
• Senner, Sonja
• Wallmichrath, Jens
• Wolff, Hans
• Giner, Tina
• Glutsch, Valerie
• Kerstan, Andreas
• Presser, Dagmar
• Schrüfer, Philipp
• Schummer, Patrick
• Stolze, Ina
• Weber, Judith
• Drexler, Konstantin
• Mickler, Marion
• Stauner, Camila Toledo

Titel

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

Ist Teil von

• European journal of cancer (1990), 2019-09, Vol.119, p.57-65

Ort / Verlag

England: Elsevier Ltd

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0–81.8%) and 59.8% (95% CI: 49.8–69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5–97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8–70.2%) and 89.2% (95% CI: 85.0–93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). •A convolutional neural network (CNN) received enhanced training with 11,444 dermoscopic images of >90% of lesions faced in a skin cancer screening setting.•The performance of 112 dermatologists from 13 university hospitals was then compared with the CNN on a test set of 300 biopsy-verified images.•The CNN achieved systematic outperformance of the dermatologists (p < 0.001) regardless of their clinical experience.