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Investigating the Impact of Attention on Mammogram Classification

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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

Attention, one of the most important features of modern CNNs, has been shown to improve the performance of mammogram classification, but our understanding of why attention offers improvements is rather limited. In this paper, we present the first comprehensive comparison of different combinations of baseline models and attention methods at multiple resolutions for whole mammogram image classification of masses and calcifications. Our findings indicate that attention generally helps to improve the baseline model scores, but the benefits are variable depending on the resolution and abnormality type. Furthermore, we find that pooling and overall model architecture (i.e., combination of baseline and attention) significantly impact mammogram classification scores. Specifically, scores are generally improved by architectural features that allow the model to retain as much information as possible while still focusing on relevant features. We also find that attention improves the correlation between model performance and LayerCAM activation in the region of interest. Our work provides insightful information to help guide the future construction of attention-based models for mammogram classification.

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Authors and Affiliations

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

    Marc Berghouse, George Bebis & Alireza Tavakkoli

Authors
  1. Marc Berghouse
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  2. George Bebis
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  3. Alireza Tavakkoli
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Corresponding author

Correspondence to Marc Berghouse .

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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Berghouse, M., Bebis, G., Tavakkoli, A. (2023). Investigating the Impact of Attention on Mammogram Classification. 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_3

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  • DOI: https://doi.org/10.1007/978-3-031-47969-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47968-7

  • Online ISBN: 978-3-031-47969-4

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