Transforming Healthcare Diagnostics With Tensorized Attention and Continual Learning on Multi-Modal Data

Analyzing multi-modal medical data in the setting of uncertain healthcare situations continues to be a major topic in medical image analysis and healthcare big data. Traditional machine learning algorithms are severely hampered by inaccurate data fusion, a lack of adaptability to changing patient da...

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Vydáno v:IEEE transactions on consumer electronics Ročník 71; číslo 2; s. 3391 - 3412
Hlavní autoři: Iqbal, Saeed, Zhong, Xiaopin, Khan, Muhammad Attique, Shabaz, Mohammad, Wu, Zongze, AlHammadi, Dina Abdulaziz, Liu, Weixiang, Algamdi, Shabbab Ali, Li, Yang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.05.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation
Artificial intelligence
Attention mechanisms
Big Data
catastrophic forgetting
continual learning
Continuing education
Data analysis
data fusion
Data integration
Data sources
Data transmission
Health care
Image analysis
Machine learning
Medical diagnostic imaging
Medical imaging
Medical services
Modal data
Multi-modal data
Neural networks
progressive neural networks (PNNs)
Regularization
Robot sensing systems
Soft sensors
tensorized attention mechanism
Uncertainty
ISSN:0098-3063, 1558-4127
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Shrnutí:Analyzing multi-modal medical data in the setting of uncertain healthcare situations continues to be a major topic in medical image analysis and healthcare big data. Traditional machine learning algorithms are severely hampered by inaccurate data fusion, a lack of adaptability to changing patient data, and challenges managing uncertainty. These difficulties are made worse by complicated medical images and diverse data sources, which results in less accurate diagnosis and worse-than-ideal healthcare choices. To tackle these urgent problems, this paper suggests two new approaches: Continual Learning using Progressive Neural Networks (PNNs) and Tensorized Attention Mechanism for Data Fusion. The Tensorized Attention Mechanism improves multi-modal data fusion by using dynamic, task-specific attention to improve feature alignment across modalities, and the PNNs framework uses continual learning, memory augmentation, and domain adaptation to ensure robust learning under data uncertainty. We test these methods on a variety of multi-modal datasets, such as MIMIC-IV, CheXpert, MOST, OAI, and Heart Murmur, which offer a comprehensive representation of medical data from clinical reports, chest X-rays, heart murmurs, and other heterogeneous data sources. Our experimental results show notable improvements in diagnostic performance, with notable results like a CFI of 0.10, a KR score of 90.4%, and an MMC score of 0.097, indicating superior generalization and robustness across domains. Healthcare AI applications could be revolutionized by the use of specialized losses, such as Conditional Variational Autoencoder (CVAE), Adversarial Contrastive Learning (ACL), Reciprocal Regularization, and domain adaptation losses, which are essential for preventing forgetting and guaranteeing learning stability across shifting data streams.
Bibliografie:ObjectType-Article-1
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ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2025.3563986