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Hybrid Region and Pixel-Level Adaptive Loss for Mass Segmentation on Whole Mammography Images

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Advances in Visual Computing (ISVC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14361))

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Abstract

Breast cancer continues to be one of the most lethal cancer types, mainly affecting women. However, thanks to the utilization of deep learning approaches for breast cancer detection, there has been a considerable boost in the performance in the field. The loss function is a core element of any deep learning architecture with a significant influence on its performance. The loss function is particularly important for tasks such as breast mass segmentation. For this task, challenging properties of input images, such as pixel class imbalance, may result in instability of training or poor detection results due to the bias of the loss function toward correctly segmenting the majority class. Inspired by the success of sample-level loss functions, we propose a hybrid loss function incorporating both pixel-level and region-level losses, where the breast tissue density is used as a sample-level weighting signal. We refer to the proposed loss as Density-based Adaptive Sample-Level Prioritizing (Density-ASP) loss. Our motivation stems from the observation that mass segmentation becomes more challenging as breast density increases. This observation makes density a viable option for controlling the effect of region-level losses. To demonstrate the effectiveness of the proposed Density-ASP, we have conducted mass segmentation experiments using two publicly available datasets: INbreast and CBIS-DDSM. Our experimental results demonstrate that Density-ASP improves segmentation performance over the commonly used hybrid losses across multiple metrics.

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Authors and Affiliations

  1. University of Nevada, Reno, NV, 89557, USA

    Parvaneh Aliniya, Mircea Nicolescu, Monica Nicolescu & George Bebis

Authors
  1. Parvaneh Aliniya
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  2. Mircea Nicolescu
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  3. Monica Nicolescu
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  4. George Bebis
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Corresponding author

Correspondence to Parvaneh Aliniya .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Google Research, Mountain View, CA, USA

    Golnaz Ghiasi

  3. New York University, New York, USA

    Yi Fang

  4. Ben-Gurion University, Be'er Sheva, Israel

    Andrei Sharf

  5. Microsoft Research, Beijing, China

    Yue Dong

  6. The University of Oklahoma, Norman, OK, USA

    Chris Weaver

  7. University of Maryland, Collage Park, MD, USA

    Zhicheng Leo

  8. University of Central Florida, Orlando, FL, USA

    Joseph J. LaViola Jr.

  9. InnerOptic Technology, Hillsborough, NC, USA

    Luv Kohli

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Aliniya, P., Nicolescu, M., Nicolescu, M., Bebis, G. (2023). Hybrid Region and Pixel-Level Adaptive Loss for Mass Segmentation on Whole Mammography Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14361. Springer, Cham. https://doi.org/10.1007/978-3-031-47969-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-47969-4_1

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  • Print ISBN: 978-3-031-47968-7

  • Online ISBN: 978-3-031-47969-4

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