Shared Embedding of X-ray & Enose Networks for Lung Cancer Classification | Proceedings of the 2023 8th International Conference on Biomedical Imaging, Signal Processing (2024)

research-article

Authors: Hung-Ju Liao, Ya-Chu Hsieh, Shih-Wen Chiu, Meng-Rui Lee, Kea-Tiong Tang, and Min Sun

ICBSP '23: Proceedings of the 2023 8th International Conference on Biomedical Imaging, Signal Processing

October 2023

Pages 9 - 16

Published: 29 January 2024 Publication History

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    Abstract

    Lung cancer is a significant cause of cancer-related deaths globally. X-ray image has been widely used for first-stage screening as it is affordable and widely available. Recently, with the development of the gas sensor IC chip, low-cost enose sensing exhaled breath from patients can potentially be used for the first-stage screening in the near future. We propose a share-embedding model combining x-ray images and enose sensory signals to diagnose lung cancer. Our model contains two branches: the image branch and the enose branch. Since the lack of the enose data, we try to use the pretrained image model to guide the enose branch to align toward the embedding space that the image model learned. Our share-embedding model is designed to be robust to domain shifts across devices and environments. To further improve performance, we use semi-supervised learning with instance weighting to transfer the model to the unlabeled target domain. To train and evaluate the performance, we collect the first paired X-ray images and enose data across multiple devices and clinical environments. In the experiments, our method outperforms each individual branch and a feature concatenation fusion method. In the cross-device setting, our method leveraging semi-supervised learning achieves the best performance.

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    Index Terms

    1. Shared Embedding of X-ray & Enose Networks for Lung Cancer Classification

      1. Applied computing

        1. Life and medical sciences

          1. Health informatics

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      Shared Embedding of X-ray & Enose Networks for Lung Cancer Classification | Proceedings of the 2023 8th International Conference on Biomedical Imaging, Signal Processing (7)

      ICBSP '23: Proceedings of the 2023 8th International Conference on Biomedical Imaging, Signal Processing

      October 2023

      127 pages

      ISBN:9798400716584

      DOI:10.1145/3634875

      Copyright © 2023 ACM.

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [emailprotected].

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      Published: 29 January 2024

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      Author Tags

      1. Lung cancer
      2. across devices and environments
      3. enose
      4. share-embedding model
      5. x-ray images

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      Shared Embedding of X-ray & Enose Networks for Lung Cancer Classification | Proceedings of the 2023 8th International Conference on Biomedical Imaging, Signal Processing (8)

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