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Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 (MICCAI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15008))

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Abstract

Current deep learning-based models typically analyze medical images in either 2D or 3D albeit disregarding volumetric information or suffering sub-optimal performance due to the anisotropic resolution of MR data. Furthermore, providing an accurate uncertainty estimation is beneficial to clinicians, as it indicates how confident a model is about its prediction. We propose a novel 2.5D cross-slice attention model that utilizes both global and local information, along with an evidential critical loss, to perform evidential deep learning for the detection in MR images of prostate cancer, one of the most common cancers and a leading cause of cancer-related death in men. We perform extensive experiments with our model on two different datasets and achieve state-of-the-art performance in prostate cancer detection along with improved epistemic uncertainty estimation. The implementation of the model is available at https://github.com/aL3x-O-o-Hung/GLCSA_ECLoss.

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Acknowledgments

The research reported herein was funded in part by the National Institutes of Health under grants R01-CA248506 and R01-CA272702 and by the Integrated Diagnostics Program of the Departments of Radiological Sciences and Pathology in the UCLA David Geffen School of Medicine.

Author information

Authors and Affiliations

  1. Department of Radiological Science, UCLA, Los Angeles, USA

    Alex Ling Yu Hung, Haoxin Zheng, Kai Zhao, Kaifeng Pang & Kyunghyun Sung

  2. Computer Science Department, UCLA, Los Angeles, USA

    Alex Ling Yu Hung, Haoxin Zheng & Demetri Terzopoulos

  3. Electrical and Computer Engineering Department, UCLA, Los Angeles, USA

    Kaifeng Pang

  4. VoxelCloud, Inc., Los Angeles, USA

    Demetri Terzopoulos

Authors
  1. Alex Ling Yu Hung
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  2. Haoxin Zheng
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  3. Kai Zhao
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  4. Kaifeng Pang
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  6. Kyunghyun Sung
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Corresponding author

Correspondence to Alex Ling Yu Hung .

Editor information

Editors and Affiliations

  1. Children’s National Hospital/George Washington University, Washington, DC, USA

    Marius George Linguraru

  2. The Chinese University of Hong Kong, Hong Kong, China

    Qi Dou

  3. Technical University of Denmark, Kgs Lyngby, Denmark

    Aasa Feragen

  4. Imperial College London, London, UK

    Stamatia Giannarou

  5. Imperial College London, London, UK

    Ben Glocker

  6. Universitat de Barcelona, Barcelona, Spain

    Karim Lekadir

  7. Helmholtz Munich, Technical University of Munich and King’s College London, Munich, Germany

    Julia A. Schnabel

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Hung, A.L.Y., Zheng, H., Zhao, K., Pang, K., Terzopoulos, D., Sung, K. (2024). Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15008. Springer, Cham. https://doi.org/10.1007/978-3-031-72111-3_11

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  • DOI: https://doi.org/10.1007/978-3-031-72111-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-72110-6

  • Online ISBN: 978-3-031-72111-3

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