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Noisy-Consistent Pseudo Labeling Model for Semi-supervised Skin Lesion Classification

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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))

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Abstract

Automated classification of skin lesions in dermoscopy images has the potential to significantly improve survival rates and reduce the risk of death for skin cancer patients. However, existing supervised learning models heavily depend on well-annotated dermoscopy training data, which is expensive and labor-intensive to obtain. This paper addresses this issue by proposing a semi-supervised framework called Noisy Consistent Pseudo Labeling (NCPL), which only utilizes less annotated images with many unlabeled raw data. The NCPL framework consists of two components: the Noisy-Consistent Sample Learning(NCSL) module to remove low-confidence images, and the Attentive Clustered Feature Integration (ACFI) module, incorporating an uncertainty-aware attention mechanism. Specifically, the NCSL module is introduced to filter and generate reliable pseudo-labels for unlabeled skin images, with excellent capability of removing noisy samples. Additionally, the ACFI module integrates high-dimensional representations of original lesion images in an attentive manner, assisted with the annotated data. By focusing the representative samples and removing noisy images, the NCPL approach performs outstanding experimental results, demonstrating the superiority of the NCPL framework in semi-supervised skin lesion classification task.

Q. Li—Supported by organization x.

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Authors and Affiliations

  1. China Aerospace Science and Industry Group 731 Hospital, Beijing, China

    Zhantao Li, Xianjun Min & Qian Li

  2. College of Computer Science and Information Engineering, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Qi Zhu

  3. R &D Department, Yizhun Medical AI Co. Ltd., Beijing, China

    Sen Li

Authors
  1. Qi Zhu
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  2. Sen Li
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  3. Zhantao Li
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  4. Xianjun Min
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  5. Qian Li
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Corresponding author

Correspondence to Qian Li .

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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Zhu, Q., Li, S., Li, Z., Min, X., Li, Q. (2023). Noisy-Consistent Pseudo Labeling Model for Semi-supervised Skin Lesion Classification. 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_22

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  • DOI: https://doi.org/10.1007/978-3-031-47425-5_22

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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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