Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multimodal fusion using sparse CCA for breast cancer survival prediction   Effective understanding of a disease such as cancer requires fusing multiple
sources of information captured across physical scales by multimodal data. In
this work, we propose a novel feature embedding module that derives from
canonical correlation analyses to account for intra-modality and inter-modality
correlations. Experiments on simulated and real data demonstrate how our
proposed module can learn well-correlated multi-dimensional embeddings. These
embeddings perform competitively on one-year survival classification of
TCGA-BRCA breast cancer patients, yielding average F1 scores up to 58.69% under
5-fold cross-validation.

3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph