Model soups improve performance of dermoscopic skin cancer classifiers
Year: 2022
Type: article
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.
Source: European Journal of Cancer
Authors: Roman C. Maron, Achim Hekler, Sarah Haggenmüller, Christof von Kalle, Jochen S. Utikal, Verena Müller, Maria Gaiser, Friedegund Meier, Sarah Hobelsberger, Frank F. Gellrich, Mildred Sergon, Axel Hauschild, Lars E. French, Lucie Heinzerling, Justin G. Schlager, Kamran Ghoreschi, Max Schlaak, Franz J. Hilke, Gabriela Poch, Sören Korsing, Carola Berking, Markus V. Heppt, Michael Erdmann, Sebastian Haferkamp, Dirk Schadendorf, Wiebke Sondermann, Matthias Goebeler, Bastian Schilling, Jakob N. Kather, Stefan Fröhling, Daniel B. Lipka, Eva Krieghoff-Henning, Titus J. Brinker
Institutions: German Cancer Research Center, National Center for Tumor Diseases, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charité – Universitätsmedizin Berlin, Heidelberg University, University Medical Centre Mannheim, University Hospital Carl Gustav Carus, Nationales Centrum für Tumorerkrankungen Dresden, Technische Universität Dresden, University Hospital Schleswig-Holstein, University of Miami, LMU Klinikum, Ludwig-Maximilians-Universität München, Humboldt-Universität zu Berlin, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen, University Hospital Regensburg, Essen University Hospital, Stiftung Universitätsmedizin Essen, Universitätsklinikum Würzburg, Universitätsklinikum Aachen, RWTH Aachen University
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,800
Funder: Bundesministerium für Gesundheit
OpenAlex ID: W4291237368