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Cited by in F6Publishing
For: Tan K, Huang W, Liu X, Hu J, Dong S. A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction. Artificial Intelligence in Medicine 2022;126:102260. [DOI: 10.1016/j.artmed.2022.102260] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Zhu X, Liu Y, Cao J, Wang X, Zhang M, Wan X, Zhou P. A multi-task prediction method for acid concentration based on attention-CLSTM.. [DOI: 10.21203/rs.3.rs-2399728/v1] [Reference Citation Analysis]
2 Ji J, Wan T, Chen D, Wang H, Zheng M, Qin Z. A deep learning method for automatic evaluation of diagnostic information from multi-stained histopathological images. Knowledge-Based Systems 2022;256:109820. [DOI: 10.1016/j.knosys.2022.109820] [Reference Citation Analysis]
3 Lu Z, Yang M, Pan C, Zheng P, Zhang S. Multi-modal Deep Learning based on the features of multi-dimensional and multi-time sequence can enhance the prognostic prediction for Multi-drug Resistant Pulmonary Tuberculosis cases. Science in One Health 2022. [DOI: 10.1016/j.soh.2022.100004] [Reference Citation Analysis]
4 Andreeva O, Ding W, Leveille SG, Cai Y, Chen P. Fall risk assessment through a synergistic multi-source DNN learning model. Artificial Intelligence in Medicine 2022;127:102280. [DOI: 10.1016/j.artmed.2022.102280] [Reference Citation Analysis]
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