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DAE-GCN: Identifying Disease-Related Features for Disease Prediction

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 12905)

Abstract

Learning disease-related representations plays a critical role in image-based cancer diagnosis, due to its trustworthy, interpretable and good generalization power. A good representation should not only be disentangled from the disease-irrelevant features, but also incorporate the information of lesion’s attributes (e.g., shape, margin) that are often identified first during cancer diagnosis clinically. To learn such a representation, we propose a Disentangle Auto-Encoder with Graph Convolutional Network (DAE-GCN), which adopts a disentangling mechanism with the guidance of a GCN model in the AE-based framework. Specifically, we explicitly separate the encoded features into disease-related features and others. Among such features that all participate in image reconstruction, we only employ the disease-related features for disease prediction. Besides, to account for lesions’ attributes, we propose to leverage the attributes and adopt the GCN to learn them during training. Take mammogram mass benign/malignant classification as an example, our DAE-GCN helps improve the performance and the interpretability of cancer prediction, which can be verified by state-of-the-art performance on one public dataset DDSM and three in-house datasets.

Keywords

  • Disease prediction
  • Disentangle
  • GCN

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Notes

  1. 1.

    We leave the number of ROIs and patients of each dataset and the description about the selection of attributes for DDSM in supplementary.

  2. 2.

    Existing works about DDSM do not publish their splitting way and mention smaller count number of ROIs in DDSM compared with our statistics.

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Acknowledgement

This work was supported by MOST-2018AAA0102004, NSFC-61625201 and ZheJiang Province Key Research & Development Program (No. 2020C03073).

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Correspondence to Xinwei Sun .

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Wang, C., Sun, X., Zhang, F., Yu, Y., Wang, Y. (2021). DAE-GCN: Identifying Disease-Related Features for Disease Prediction. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_5

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  • DOI: https://doi.org/10.1007/978-3-030-87240-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87239-7

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