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Privacy Preserving and Communication Efficient Information Enhancement for Imbalanced Medical Image Classification

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Medical Image Understanding and Analysis (MIUA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13413))

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Abstract

Deep learning methods, especially convolutional neural networks, have become more and more popular in medical image classifications. However, training a deep neural network from scratch can be a luxury for many medical image datasets as the process requires a large and well-balanced sample to output satisfactory results. Unlike natural image datasets, medical images are expensive to collect owing to labor and equipment costs. Besides, the class labels in medical image datasets are usually severely imbalanced subject to the availability of patients. Further, aggregating medical images from multiple sources can be challenging due to policy restrictions, privacy concerns, communication costs, and data heterogeneity caused by equipment differences and labeling discrepancies. In this paper, we propose to address these issues with the help of transfer learning and artificial samples created by generative models. Instead of requesting medical images from source data, our method only needs a parsimonious supplement of model parameters pre-trained on the source data. The proposed method preserves the data privacy in the source data and significantly reduces the communication cost. Our study shows transfer learning together with artificial samples can improve the pneumonia classification accuracy on a small but heavily imbalanced chest X-ray image dataset by \(11.53\%\) which performs even better than directly augmenting that source data into the training process.

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Notes

  1. 1.

    Available at https://data.mendeley.com/datasets/rscbjbr9sj/2.

  2. 2.

    Available at https://nihcc.app.box.com/v/ChestXray-NIHCC.

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Authors and Affiliations

  1. University of Georgia, Athens, GA, 30601, USA

    Xiaochuan Li & Yuan Ke

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  1. Xiaochuan Li
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  2. Yuan Ke
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Correspondence to Yuan Ke .

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Editors and Affiliations

  1. Imperial College London, London, UK

    Guang Yang

  2. University of Cambridge, Cambridge, UK

    Angelica Aviles-Rivero

  3. University of Cambridge, Cambridge, UK

    Michael Roberts

  4. University of Cambridge, Cambridge, UK

    Carola-Bibiane Schönlieb

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Li, X., Ke, Y. (2022). Privacy Preserving and Communication Efficient Information Enhancement for Imbalanced Medical Image Classification. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham. https://doi.org/10.1007/978-3-031-12053-4_49

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  • DOI: https://doi.org/10.1007/978-3-031-12053-4_49

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