CAMDA Net: Cross Attention Multi-modal Domain Adaptation using B-mode and Sinogram

Lung ultrasound (LUS) is a valuable imaging technique in point-of-care (POC) for the prompt diagnosis of lung disease, but accurately identifying specific diseases can be challenging. Recently, learning-based lung disease classification networks have shown potential for clinical applications. Howeve...

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Main Authors: Lee, Hyeonjik, Oh, Seok-Hwan, Kim, Myeong-Gee, Kim, Young-Min, Jung, Guil, Kim, Sang-Yun, Kwon, Hyuk-Sool, Bae, Hyeon-Min
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Biomarkers
Cross attention
Deep learning
Domain adaptation
Focusing
Frequency control
Lung ultrasound
Lungs
Performance evaluation
Point of care
Pulmonary diseases
Sinogram
Supervised learning
Ultrasonic imaging
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Summary:Lung ultrasound (LUS) is a valuable imaging technique in point-of-care (POC) for the prompt diagnosis of lung disease, but accurately identifying specific diseases can be challenging. Recently, learning-based lung disease classification networks have shown potential for clinical applications. However, current approaches rely on supervised learning using LUS data from cart-type devices, leading to a domain gap when using lower-quality LUS from POC hand-held (HH) devices. In this paper, we proposed Cross Attention Multi-modal Domain Adaptation (CAMDA) lung classification network, which aims to address this domain gap. CAMDA-net leverages both B-mode and sinogram modalities, using cross-attention to focus on domain-invariant biomarkers while minimizing device-specific dependencies. The proposed domain adaptation scheme ensures robust performance even with unseen LUS data from HH devices. Quantitative results demonstrate that CAMDA-net significantly enhances balanced accuracy by 35.4% over baseline models, confirming its effectiveness in mitigating domain discrepancies.
ISSN:2375-0448
DOI:10.1109/UFFC-JS60046.2024.10794084