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Cross Domain Pulmonary Nodule Detection Without Source Data

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AI 2023: Advances in Artificial Intelligence (AI 2023)

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Abstract

The model performance on cross-domain pulmonary nodule detection usually degrades because of the significant shift in data distributions and the scarcity of annotated medical data in the test scenarios. Current approaches to cross-domain object detection assume that training data from the source domain are freely available; however, such an assumption is implausible in the medical field, as the data are confidential and cannot be shared due to privacy concerns. Thus, this paper introduces source data-free cross-domain pulmonary nodule detection. In this setting, only a pre-trained model from the source domain and a few annotated samples from the target domain are available. We introduce a novel method to tackle this issue, adapting the feature extraction module for the target domain through minimizing the proposed General Entropy (GE). Specifically, we optimize the batch normalization (BN) layers of the model by GE minimization. Thus, the dataset-level statistics of the target domain are utilized for optimization and inference. Furthermore, we tune the detection head of the model using annotated target samples to mitigate the rater difference and improve the accuracy. Extensive experiments on three different pulmonary nodule datasets show the efficacy of our method for source data-absent cross-domain pulmonary nodule detection.

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Acknowledgements

This work was partially supported by the Special Fund of Hubei Luojia Laboratory under Grant 220100014, and the Fundamental Research Funds for the Central Universities (No. 2042023kf1033).

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Authors and Affiliations

  1. Wuhan University, Wuhan, 430072, China

    Rui Xu & Yong Luo

  2. The University of Chicago, Chicago, IL, 60637, USA

    Yan Xu

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  1. Rui Xu
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  2. Yong Luo
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  3. Yan Xu
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Correspondence to Yong Luo .

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Editors and Affiliations

  1. The University of Sydney, Darlington, NSW, Australia

    Tongliang Liu

  2. Monash University, Clayton, VIC, Australia

    Geoff Webb

  3. The University of Newcastle, Callaghan, NSW, Australia

    Lin Yue

  4. CSIRO Data61, Sydney, NSW, Australia

    Dadong Wang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Xu, R., Luo, Y., Xu, Y. (2024). Cross Domain Pulmonary Nodule Detection Without Source Data. In: Liu, T., Webb, G., Yue, L., Wang, D. (eds) AI 2023: Advances in Artificial Intelligence. AI 2023. Lecture Notes in Computer Science(), vol 14471. Springer, Singapore. https://doi.org/10.1007/978-981-99-8388-9_13

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  • DOI: https://doi.org/10.1007/978-981-99-8388-9_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8387-2

  • Online ISBN: 978-981-99-8388-9

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