Abstract
Multitasking learning improves a model’s ability to generalize by learning multiple tasks in parallel. However, it is difficult to know how each task influences the others’ learning. In this work, we study in-depth the behavior of the tasks of skin lesion segmentation, hair mask segmentation, and the inpainting of those hairs, in a multitasking framework to discover how they influence each other. The experiments are performed using an encoder-decoder convolutional neural network and images from five public databases: PH2, dermquest, dermis, EDRA2002, and the ISIC Data Archive. To evaluate the tasks’ performance, we use a series of metrics on which we apply a statistical test to check the superiority of each task in a multitasking model with respect to their individual performance. We also check, in a three-task model, whether there is a task that dominates the learning stage. Finally, we conclude that while the inpainting task does not benefit from this type of learning, the rest of the tasks improve their performance when compared to that obtained by their corresponding single-task model.
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Acknowledgments
This work was partially supported by the Spanish Grant FEDER/Ministerio de Economía, Industria y Competitividad - AEI/TIN2016-75404-P. Lidia Talavera-Martínez also benefited from the fellowship BES-2017-081264 conceded by the Ministry of Economy, Industry and Competitiveness under a program co-financed by the European Social Fund. We thank M. Attia from the Deakin University, Australia, for providing the GAN-based simulated hair images.
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Talavera-Martínez, L., Bibiloni, P., González-Hidalgo, M. (2021). A Multitasking Learning Framework for Dermoscopic Image Analysis. In: Tavares, J.M.R.S., Papa, J.P., González Hidalgo, M. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2021. Lecture Notes in Computer Science(), vol 12702. Springer, Cham. https://doi.org/10.1007/978-3-030-93420-0_4
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