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Leveraging Contrastive Learning with SimSiam for the Classification of Primary and Secondary Liver Cancers

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14394))

  • 196 Accesses

Abstract

Accurate liver cancer classification is essential, as it substantially influences the selection of effective treatment strategies and impacts patient prognosis. Convolutional neural network (CNN) classifiers typically require extensive labeled datasets for training to attain decent performance. However, the process of obtaining labeled data through manual labeling is time-consuming, potentially biased, and costly when applied to large datasets. This study utilizes the Simple Siamese (SimSiam) contrastive self-supervised learning approach to enhance the classification of liver tumours, especially considering the limited availability of labeled computed tomography (CT) scans of liver cancer. We integrate SimSiam with three baseline CNN-based classifiers - Inception, Xception, and ResNet152 - and pretrain them with two loss functions: mean squared error (MSE) and cosine similarity (COS). Our findings show consistent improvements for three classifiers compared to the baseline models. Specifically, the ResNet152 model exhibits the highest performance among the evaluated networks. With MSE and COS losses, the classification accuracy for ResNet152 improves by 1.27% and 2.53%, respectively. The classification accuracy of the Inception model improves by 3.95% and 5.26%. Similarly, Xception’s validation accuracy demonstrates an increase of 2.60% with both loss functions, compared to the baseline models. We validate our pipeline via our multi-resolution in-house abdominal CT scans of primary and secondary liver cancers, including 155 patients with hepatocellular carcinoma, 198 patients with colorectal liver metastases, and 107 patients with intrahepatic cholangiocarcinoma. Source code available at:https://github.com/Ramtin-Mojtahedi/SimSiam-LiverCancer-CL.

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Acknowledgements

This work was funded by National Institutes of Health and National Cancer Institute grants R01CA233888 and U01CA238444.

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Authors and Affiliations

  1. School of Computing, Queen’s University, Kingston, ON, Canada

    Ramtin Mojtahedi, Mohammad Hamghalam & Amber L. Simpson

  2. Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Mohammad Hamghalam

  3. Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    William R. Jarnagin

  4. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Richard K. G. Do

  5. Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada

    Amber L. Simpson

Authors
  1. Ramtin Mojtahedi
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  2. Mohammad Hamghalam
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  3. William R. Jarnagin
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  4. Richard K. G. Do
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  5. Amber L. Simpson
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Corresponding author

Correspondence to Amber L. Simpson .

Editor information

Editors and Affiliations

  1. Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Jonghye Woo

  2. Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

    Alessa Hering

  3. The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Wilson Silva

  4. Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Xiang Li

  5. A*STAR, Institute of High Performance Computing, Singapore, Singapore

    Huazhu Fu

  6. Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Xiaofeng Liu

  7. Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Fangxu Xing

  8. University of Maryland Baltimore County, Baltimore, MD, USA

    Sanjay Purushotham

  9. National Institutes of Health, Bethesda, MD, USA

    Tejas S. Mathai

  10. National Institutes of Health, Bethesda, MD, USA

    Pritam Mukherjee

  11. Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

    Max De Grauw

  12. The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Regina Beets Tan

  13. The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Valentina Corbetta

  14. University Hospital Freiburg, Freiburg, Germany

    Elmar Kotter

  15. University of Bern, Bern, Switzerland

    Mauricio Reyes

  16. University of Tübingen, Tübingen, Germany

    Christian F. Baumgartner

  17. Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

    Quanzheng Li

  18. University of Southern California, Los Angeles, CA, USA

    Richard Leahy

  19. Peking University, Beijing, China

    Bin Dong

  20. Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Hao Chen

  21. Vanderbilt University, Nashville, TN, USA

    Yuankai Huo

  22. University of Sydney, Camperdown, NSW, Australia

    Jinglei Lv

  23. Institute of High Performance Computing, Singapore, Singapore

    Xinxing Xu

  24. Hong Kong University of Science and Technology, Hong Kong, China

    Xiaomeng Li

  25. Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Dwarikanath Mahapatra

  26. University of Alberta, Edmonton, AB, Canada

    Li Cheng

  27. LITIS, University of Rouen, Rouen, France

    Caroline Petitjean

  28. ImViA Laboratory, University of Burgundy, Dijon, France

    Benoît Presles

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Mojtahedi, R., Hamghalam, M., Jarnagin, W.R., Do, R.K.G., Simpson, A.L. (2023). Leveraging Contrastive Learning with SimSiam for the Classification of Primary and Secondary Liver Cancers. In: Woo, J., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops. MICCAI 2023. Lecture Notes in Computer Science, vol 14394. Springer, Cham. https://doi.org/10.1007/978-3-031-47425-5_28

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  • DOI: https://doi.org/10.1007/978-3-031-47425-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47424-8

  • Online ISBN: 978-3-031-47425-5

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