Multimodal fusion using sparse CCA for breast cancer survival prediction
dc.contributor.author | Subramanian, Vaishnavi | |
dc.contributor.author | Syeda-Mahmood, Tanveer | |
dc.contributor.author | Do, N. Minh | |
dc.date.accessioned | 2024-08-23T03:25:48Z | |
dc.date.available | 2024-08-23T03:25:48Z | |
dc.date.issued | 2021-04-13 | |
dc.identifier.uri | https://vinspace.edu.vn/handle/VIN/236 | |
dc.description.abstract | Effective 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.iso | en_US | en_US |
dc.title | Multimodal fusion using sparse CCA for breast cancer survival prediction | en_US |
dc.type | Article | en_US |
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Minh Do, PhD. [5]
Honorary Vice Provost