Abstract
This paper proposes a novel loss function inspired by the Tversky-Kahneman probability weighting function to effectively deal with medical image segmentation tasks. The proposed loss, which is called Tversky-Kahneman loss function, is assessed on the official Skin Lesion datasets of the ISIC 2017 and ISIC 2018 Challenge. To evaluate our new loss function, we propose modified U-Net-based model to obtain quantitative results in Dice score and Jaccard index. Various experiments indicate that our new loss function provides a more promising and time-saving performance than other loss functions.
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Acknowledgements
This research is funded by the Hanoi University of Science and Technology (HUST) under project number T2021-PC-005. Minh-Nhat Trinh was funded by Vingroup JSC and supported by the Master, Ph.D. Scholarship Program of Vingroup Innovation Foundation (VINIF), Institute of Big Data, code VINIF.2021.ThS.33.
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Nham, DHN., Trinh, MN., Pham, VT., Tran, TT. (2023). Tversky-Kahneman: A New Loss Function for Skin Lesion Image Segmentation. In: Saraswat, M., Chowdhury, C., Kumar Mandal, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 551. Springer, Singapore. https://doi.org/10.1007/978-981-19-6631-6_14
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DOI: https://doi.org/10.1007/978-981-19-6631-6_14
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