Abstract
Deep learning has great prevalence in various medical diagnosis tasks. Existing methods can tackle the issue of multiviews very well. However, these methods cannot process indefinite lengths of multiviews, especially with a “dimension gap” between them, such as blood flow ultrasound images. In this work, we propose Latent Representation Weight Learning (LRWL) to learn the latent representative weight of each image or view and then integrate the views with the weights and the diagnostic indexes as part of the input data to DL to predict successful conception. This method can describe the role of each view accurately. We perform thorough experiments on a real reproduction dataset to evaluate LRWL. The results show that our proposed method achieves the top performances with higher accuracy and good convergence.
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Acknowledgements
We would like to acknowledge the financial support in part by the Shandong Natural Science Foundation (ZR2021M F068, ZR2021MF015, ZR2021MF107, ZR2021QF134), Shandong Computer Society Provincial Key Laboratory Joint Open Fund (SKLCN-2020-06), Wealth Management Characteristic Construction Project of Shandong Technology and Business University (2019ZBKY032).
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Li, B., Sun, M., Yu, Y., Zhao, Y., Xiang, Z., An, Z. (2023). Latent Representation Weights Learning of the Indefinite Length of Views for Conception Diagnosis. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) Multimodal AI in Healthcare. Studies in Computational Intelligence, vol 1060. Springer, Cham. https://doi.org/10.1007/978-3-031-14771-5_8
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