Multimodal Deep Learning for Cervical Dysplasia Diagnosis
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Neural networks can leverage additional data by integrating multiple modalities into a general framework.
“Previous works demonstrate the ability of neural networks to leverage additional data by integrating multiple modalities into a general framework.[11,27,28]”
From Multimodal skin lesion classification using deep learning (2018) · cites this paper as [27]
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis
Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968 · see citing works
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
Deep Learning for Health Informatics
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636665 · Cited by 2,000 · see citing works
Multimodal skin lesion classification using deep learning
Yap, J., Yolland, W., & Tschandl, P. (2018) · Experimental Dermatology · doi:10.1111/exd.13777 · Cited by 326 · see citing works
Li, Y., El Habib Daho, M., Conze, P.-H., Zeghlache, R., Le Boité, H., Tadayoni, R., Cochener, B., Lamard, M., & Quellec, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108635 · Cited by 274 · see citing works
Artificial intelligence-based methods for fusion of electronic health records and imaging data
Mohsen, F., Ali, H., El Hajj, N., & Shah, Z. (2022) · Scientific Reports · doi:10.1038/s41598-022-22514-4 · Cited by 250 · see citing works
Yoo, Y., Tang, L. Y. W., Li, D. K. B., Metz, L., Kolind, S., Traboulsee, A. L., & Tam, R. C. (2019) · Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization · doi:10.1080/21681163.2017.1356750 · Cited by 67 · see citing works
- Authors
- Xu, T., Zhang, H., Huang, X., Zhang, S., & Metaxas, D. N.
- Affiliations
- Lehigh University (US)
- Rutgers, The State University of New Jersey (US)
- University of North Carolina at Charlotte (US)
- Corresponding author
- Huang, Xiaolei
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 115-123
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2526421605
- Cited by
- 187
- References
- 2
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 20 · 2023: 12 · 2022: 26 · 2021: 34 · 2020: 21 · 2019: 30 · 2018: 19 · 2017: 8 · 2016: 2
- FWCI
- 10.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Epidemiology › Cervical Cancer and HPV Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- cervical intraepithelial neoplasia, multimodal fusion, cervical cancer screening, convolutional neural networks, feature fusion, end-to-end deep learning, sensitivity and specificity
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