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dc.contributor.authorSubramanian, Vaishnavi
dc.contributor.authorSyeda-Mahmood, Tanveer
dc.contributor.authorDo, N. Minh
dc.date.accessioned2024-08-23T03:25:48Z
dc.date.available2024-08-23T03:25:48Z
dc.date.issued2021-04-13
dc.identifier.urihttps://vinspace.edu.vn/handle/VIN/236
dc.description.abstractEffective understanding of diseases like cancer necessitates integrating diverse information across various physical scales through multimodal data. In this study, we introduce a novel feature embedding module based on canonical correlation analysis (CCA) to capture both intra-modality and inter-modality correlations. Our approach leverages CCA to develop multi-dimensional embeddings that align well across different data sources. We validated our method using both simulated and real datasets, demonstrating its capability to generate well-correlated embeddings. When applied to the one-year survival classification of breast cancer patients from the TCGA-BRCA dataset, our embeddings achieved competitive performance, with average F1 scores reaching up to 58.69% in 5-fold cross-validation.en_US
dc.language.isoen_USen_US
dc.titleMultimodal fusion using sparse CCA for breast cancer survival predictionen_US
dc.typeArticleen_US


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