Abstract
Symmetry is one of the distinguishing features when diagnosing the malignancy of skin lesions. In this work, we introduce an extension of the SymDerm dataset with around 2000 new annotations, and analyze 1) the effect of different data augmentation techniques on learning the skin lesion symmetry classification task, and 2) how the learning of this task is affected when combined with the classification of its malignancy in a multitask learning environment. We conclude that, although not all data augmentation techniques improve classification performance, these techniques achieve an increase of approximately 7.7% for B.Acc and Precision, 8.0% for Recall and F1-score, and 15.08% for the Kappa score. Moreover, we show that symmetry classification benefits from the introduction of an auxiliary task by stabilizing the learning curve and decreasing the train-validation learning gap.
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Acknowledgements
This paper is part of the R+D+i Project PID2020-113870GB-I00 - “Desarrollo de herramientas de Soft Computing para la Ayuda al Diagnóstico Clínico y a la Gestión de Emergencias (HESOCODICE)”, funded by MCIN/AEI/10.13039/501100011033/.
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Talavera-Martínez, L., Bibiloni, P., Giacaman, A., Taberner, R., Del Pozo Hernando, L.J., González-Hidalgo, M. (2023). Model Regularisation for Skin Lesion Symmetry Classification: SymDerm v2.0. In: Tsapatsoulis, N., et al. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14184. Springer, Cham. https://doi.org/10.1007/978-3-031-44237-7_10
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