Abstract
Alarming statistics on the mortality rate for breast cancer are a clear indicator of the significance of computer vision tasks related to cancer identification. In this study, we focus on mass segmentation, which is a crucial task for cancer identification as it preserves critical properties of the mass, such as shape and size, vital for identification tasks. While achieving promising results, existing approaches are mostly hindered by pixel class imbalance and various mass sizes that are inherent properties of masses in mammography images. We propose to alleviate this limitation on segmentation methods via a novel modification of the common hybrid loss, which is a weighted sum of the cross entropy and dice loss. The proposed loss, termed Adaptive Sample-Level Prioritizing (ASP) loss, leverages the higher-level information presented in the segmentation mask for customizing the loss for every sample, to prioritize the contribution of each loss term accordingly. As one of the variations of U-Net, AU-Net is selected as the baseline approach for the evaluation of the proposed loss. The ASP loss could be integrated with other existing mass segmentation approaches to enhance their performance by providing them with the ability to address the problems associated with the pixel class imbalance and diverse mass sizes specific to the domain of breast mass segmentation. We tested our method on two publicly available datasets, INbreast and CBIS-DDSM. The results of our experiments show a significant boost in the performance of the baseline method while outperforming state-of-the-art mass segmentation methods.
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Aliniya, P., Nicolescu, M., Nicolescu, M., Bebis, G. (2023). ASP Loss: Adaptive Sample-Level Prioritizing Loss for Mass Segmentation on Whole Mammography Images. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14255. Springer, Cham. https://doi.org/10.1007/978-3-031-44210-0_9
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