A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data
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Liu et al. [86] and Sun et al. [87] learned the weights of the predictions by their sub-models as hyperparameters on a validation set.
“Liu et al. [86] and Sun et al. [87] learned the weights of the predictions by their sub-models as hyperparameters on a validation set.”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [86]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576 · see citing works
Self-citationShin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects
Lahat, D., Adali, T., & Jutten, C. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2460697 · Cited by 1,296 · see citing works
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Multimodal deep learning for biomedical data fusion: a review
Stahlschmidt, S. R., Ulfenborg, B., & Synnergren, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab569 · Cited by 766 · see citing works
Multimodal data integration for oncology in the era of deep neural networks: a review
Waqas, A., Tripathi, A., Ramachandran, R. P., Stewart, P. A., & Rasool, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1408843 · Cited by 165 · see citing works
RETRACTED ARTICLE: Deep learning approaches to detect breast cancer: a comprehensive review
Sharafaddini, A. M., Esfahani, K. K., & Mansouri, N. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20011-6 · Cited by 104 · see citing works
RetractedRadioPathomics: Multimodal learning in non-small cell lung cancer for adaptive radiotherapy
Tortora, M., Cordelli, E., Sicilia, R., Nibid, L., Ippolito, E., Perrone, G., Ramella, S., & Soda, P. (2023) · IEEE Access · doi:10.1109/access.2023.3275126 · Cited by 49 · see citing works
- Authors
- Liu, T., Huang, J., Liao, T., Pu, R., Liu, S., & Peng, Y.
- Affiliations
- Sichuan Agricultural University (CN)
- Corresponding author
- Liu, T.
- Venue
- IRBM
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 43
- Issue
- 1
- Pages
- 62-74
- ISSN
- 1876-0988 (ISSN-L)
- 1959-0318
- OpenAlex
- W3118914438
- Cited by
- 137
- References
- 6
- Citations by year
- 2026: 23 · 2025: 45 · 2024: 27 · 2023: 26 · 2022: 10 · 2021: 6
- FWCI
- 6.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IRBM (publishedVersion)
- Funders
- Sichuan Agricultural University (201910626061)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- breast cancer molecular subtypes, multimodal fusion, gene expression data, histopathological images, hybrid deep learning model, dimensionality reduction, TCGA-BRCA
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