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Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects.

Elisa Warner¹, Joonsang Lee¹, William Hsu²

¹Department of Computational Medicine and Bioinformatics, University of Michigan Ann Arbor, 100 Washtenaw Ave., Ann Arbor, MI 48109 USA.

International Journal of Computer Vision

|August 30, 2024

Summary

This summary is machine-generated.

This survey explores multimodal machine learning (ML) in medical AI, highlighting its potential for clinical predictions and decision support. Challenges like data bias and the need for robust integration are discussed.

Keywords:

Alignment Artificial intelligence Co-learning Data integration Fusion Machine learning Multimodal Representation Translation

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Area of Science:

Medical Artificial Intelligence (AI)
Machine Learning (ML)

Background:

Shift from traditional methods to deep learning in medical AI.
Growing application of multimodal machine learning in healthcare.

Purpose of the Study:

Navigate the current landscape of multimodal ML in medical AI.
Focus on impact on medical image analysis and clinical decision support.
Explore potential for clinical predictions using multimodal models.

Main Methods:

Survey of multimodal machine learning techniques.
Emphasis on representation, fusion, translation, alignment, and co-learning.
Discussion of challenges and innovations in multimodal model development.

Main Results:

Multimodal ML shows transformative potential for clinical predictions.
Highlights need for principled assessments and practical implementation.
Identifies persistent challenges like data biases and big data scarcity.

Conclusions:

Advancements in multimodal ML offer significant promise for healthcare.
Need for collaborative efforts to integrate models seamlessly into biomedical practice.
Importance of addressing data biases and ensuring practical implementation.