Predicting molecular subtypes of breast cancer using multimodal deep learning and incorporation of the attention mechanismDownload PDF

Apr 04, 2021 (edited Jun 01, 2021)MIDL 2021 Conference Short SubmissionReaders: Everyone
  • Keywords: Multimodal Deep Learning, Breast Cancer, Molecular Subtypes, Convolutional Neural Network, Attention Mechanism, Mammography, Ultrasound
  • TL;DR: To predict the molecular subtypes of breast cancer using multimodal deep learning with attention mechanism.
  • Abstract: Accurately determining the molecular subtype of breast cancer is an important factor for the prognosis of breast cancer patients, and can guide treatment selection. In this study, we report a multimodal deep learning with attention mechanism (MDLA) for predicting the molecular subtypes of breast cancer from mammography and ultrasound images. Incorporation of the attention mechanism improved diagnostic performance for predicting 4-class molecular subtypes with Matthews correlation coefficient (MCC) of 0.794. The MDLA can also discriminate between Luminal disease and non-luminal disease with areas under the receiver operating characteristic curve (AUC) of 0.855. This work thus provides a noninvasive imaging biomarker to predict the molecular subtypes of breast cancer.
  • Paper Type: both
  • Source Latex: zip
  • Primary Subject Area: Detection and Diagnosis
  • Secondary Subject Area: Application: Radiology
  • Paper Status: original work, not submitted yet
  • Source Code Url: The source code we developed will be publicly available on "https://github.com/Tianyu-Zhang0319/Multimodal_Deep_Learning". If you have any questions, please contact the author (t.zhang@nki.nl).
  • Data Set Url: The data used in this study is not a public data set, but comes from the Netherlands Cancer Institute. So it is not convenient to make it public. We will publicly provide some data examples on "https://github.com/Tianyu-Zhang0319/Multimodal_Deep_Learning". If you have any questions, please contact the author (t.zhang@nki.nl).
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  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
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