Model soups improve performance of dermoscopic skin cancer classifiers. (September 2022)

• Record Type: Journal Article
• Authors: Maron, Roman C.; Hekler, Achim; Haggenmüller, Sarah; von Kalle, Christof; Utikal, Jochen S.; Müller, Verena; Gaiser, Maria; Meier, Friedegund; Hobelsberger, Sarah; Gellrich, Frank F.; Sergon, Mildred; Hauschild, Axel; French, Lars E.; Heinzerling, Lucie; Schlager, Justin G.; Ghoreschi, Kamran; Schlaak, Max; Hilke, Franz J.; Poch, Gabriela; Korsing, Sören; Berking, Carola; Heppt, Markus V.; Erdmann, Michael; Haferkamp, Sebastian; Schadendorf, Dirk; Sondermann, Wiebke; Goebeler, Matthias; Schilling, Bastian; Kather, Jakob N.; Fröhling, Stefan; Lipka, Daniel B.; Krieghoff-Henning, Eva; Brinker, Titus J.;
• Is Part Of: European journal of cancer. Volume 173(2022)
• Page Start: 307
• Page End: 316
• Publication Date: 2022-09
• Subjects: Dermatology -- Melanoma -- Nevus -- Artificial intelligence -- Deep learning -- Ensembles -- Model soups -- Robustness -- Generalisation -- Calibration; AUROC area under the receiver operating characteristic -- BCE balanced corruption error -- BS brier score -- CNN convolutional neural network -- DA data augmentation -- DL deep learning -- ECE expected calibration error -- FR flip rate -- INV inverse -- mBCE mean balanced corruption error -- mFR mean flip rate -- NLL negative log likelihood -- SD standard deviation; Cancer -- Periodicals; Neoplasms -- Periodicals; Cancer -- Périodiques; Cancer; Tumors; Electronic journals; Periodicals; Electronic journals; 616.994
• DOI: 10.1016/j.ejca.2022.07.002
• Languages: English
• ISSNs: 0959-8049

Abstract

Abstract: Background: Image-based cancer classifiers suffer from a variety of problems which negatively affect their performance. For example, variation in image brightness or different cameras can already suffice to diminish performance. Ensemble solutions, where multiple model predictions are combined into one, can improve these problems. However, ensembles are computationally intensive and less transparent to practitioners than single model solutions. Constructing model soups, by averaging the weights of multiple models into a single model, could circumvent these limitations while still improving performance. Objective: To investigate the performance of model soups for a dermoscopic melanoma-nevus skin cancer classification task with respect to (1) generalisation to images from other clinics, (2) robustness against small image changes and (3) calibration such that the confidences correspond closely to the actual predictive uncertainties. Methods: We construct model soups by fine-tuning pre-trained models on seven different image resolutions and subsequently averaging their weights. Performance is evaluated on a multi-source dataset including holdout and external components. Results: We find that model soups improve generalisation and calibration on the external component while maintaining performance on the holdout component. For robustness, we observe performance improvements for pertubated test images, while the performance on corrupted test images remains on par. Conclusions: Overall, souping for skin cancer classifiers has a positive effect on generalisation, robustness and calibration. It is easy for practitioners to implement and by combining multiple models into a single model, complexity is reduced. This could be an important factor in achieving clinical applicability, as less complexity generally means more transparency. Highlights: Artificial intelligence-based skin cancer classifiers suffer from generalisation and robustness issues. Ensemble solutions improve these issues but are expensive and complex. Model soups combine multiple models into a single model of equal size and complexity. Model soups improve classifier performance on images from other clinics. They also have a positive effect on model robustness and calibration.