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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Was deactivated on December 31, 2019.
- 2.
- 3.
References
Argenziano, G., Soyer, H., De Giorgi, V., Piccolo, D., Carli, P., Delfino, M.: Interactive atlas of dermoscopy (Book and CD-ROM). EDRA Medical Publishing & New media (2000)
Attia, M., Hossny, M., Zhou, H., Nahavandi, S., Asadi, H., Yazdabadi, A.: Realistic hair simulator for skin lesion images: a novel benchemarking tool. Artif. Intell. Med. 108, 101933 (2020)
Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41–75 (1997)
Chen, E.Z., Dong, X., Li, X., Jiang, H., Rong, R., Wu, J.: Lesion attributes segmentation for melanoma detection with multi-task u-net. In: IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 485–488. IEEE (2019)
Chen, Z., Badrinarayanan, V., Lee, C.Y., Rabinovich, A.: Gradnorm: gradient normalization for adaptive loss balancing in deep multitask networks. In: International Conference on Machine Learning, pp. 794–803. PMLR (2018)
Kawahara, J., Daneshvar, S., Argenziano, G., Hamarneh, G.: Seven-point checklist and skin lesion classification using multitask multimodal neural nets. IEEE J. Biomed. Health Inform. 23(2), 538–546 (2018)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Mendonça, T., Celebi, M., Mendonça, T., Marques, J.: \({PH}^2\): a public database for the analysis of dermoscopic images. In: Dermoscopy Image Analysis (2015)
Mirzaalian, H.: Hair Sim Software. http://www2.cs.sfu.ca/~hamarneh/software/hairsim/Welcome.html. Accessed 7 Mar 2019
Ruder, S.: An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017)
Song, L., Lin, J.P., Wang, Z.J., Wang, H.: An end-to-end multi-task deep learning framework for skin lesion analysis. IEEE J. Biomed. Health Inform. 24(10), 2912–2921 (2020)
Talavera-Martínez, L., Bibiloni, P., González-Hidalgo, M.: Hair segmentation and removal in dermoscopic images using deep learning. IEEE Access 9, 2694–2704 (2021). https://doi.org/10.1109/ACCESS.2020.3047258
Talavera-Martínez, L., Bibiloni, P., González-Hidalgo, M.: How do different tasks influence a multitasking environment? Case study in dermoscopic images. In: To be Published at IEEE 18th International Symposium on Biomedical Imaging (ISBI 2021) (2021, in press)
Xie, F.Y., Qin, S.Y., Jiang, Z.G., Meng, R.S.: PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma. Comput. Med. Imaging Graph. 33(4), 275–282 (2009)
Yang, X., Zeng, Z., Yeo, S.Y., Tan, C., Tey, H.L., Su, Y.: A novel multi-task deep learning model for skin lesion segmentation and classification. arXiv preprint arXiv:1703.01025 (2017)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-93420-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-93419-4
Online ISBN: 978-3-030-93420-0
eBook Packages: Computer ScienceComputer Science (R0)
