Metric Learning on Healthcare Data with Incomplete Modalities
Open accessAbstract
Utilizing multiple modalities to learn a good distance metric is of vital importance for various clinical applications. However, it is common that modalities are incomplete for some patients due to various technical and practical reasons in healthcare datasets. Existing metric learning methods cannot directly learn the distance metric on such data with missing modalities. Nevertheless, the incomplete data contains valuable information to characterize patient similarity and modality relationships, and they should not be ignored during the learning process. To tackle the aforementioned challenges, we propose a metric learning framework to perform missing modality completion and multi-modal metric learning simultaneously. Employing the generative adversarial networks, we incorporate both complete and incomplete data to learn the mapping relationship between modalities. After completing the missing modalities, we use the nonlinear representations extracted by the discriminator to learn the distance metric among patients. Through jointly training the adversarial generation part and metric learning, the similarity among patients can be learned on data with missing modalities. Experimental results show that the proposed framework learns more accurate distance metric on real-world healthcare datasets with incomplete modalities, comparing with the state-of-the-art approaches. Meanwhile, the quality of the generated modalities can be preserved.
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Real-world multimodal data are often imperfect because some modalities are missing.
“Multimodal data collected from the real world are often imperfect due to missing modalities.”
From Are Multimodal Transformers Robust to Missing Modality? (2022) · cites this paper as [37]
The Abstract and Introduction say missing modalities are common in healthcare datasets, including data collected from patients in real-world settings. The cited paper does not establish that this is often true of real-world multimodal data generally, so the claim extends beyond its stated scope. (LLM, conf=0.83)
Experimental — accuracy not yet measuredMeLIM proposed a framework to impute missing modalities via a GAN framework and learned joint sample representations.
“The MeLIM method proposed a framework to impute the missing modalities via a GAN framework and learned the joint sample representations [150].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [150]
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- Authors
- Suo, Q., Zhong, W., Ma, F., Yuan, Y., Gao, J., & Zhang, A.
- Affiliations
- University at Buffalo, State University of New York (US)
- Beijing University of Technology (CN)
- University of Virginia (US)
- Venue
- Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
- Published
- 2019-08
- Pages
- 3534-3540
- OpenAlex
- W2965487497
- Cited by
- 46
- References
- 25
- Citations by year
- 2025: 17 · 2024: 4 · 2023: 12 · 2022: 6 · 2021: 2 · 2020: 5
- FWCI
- 1.24
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Funders
- National Science Foundation (1553411)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- metric learning, incomplete modalities, generative adversarial networks, patient similarity, health data
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