Authors: Xuran Zhu

Published on: May 06, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2405.03235

Summary

• What is new: Employing the Maximum Mean Difference (MMD) method combined with Convolutional Neural Networks (CNNs) for enhancing cross-modality domain adaptation between CT and MRI images.
• Why this is important: The scarcity of annotated data for training machine learning models in diagnosing brain disorders using medical imaging techniques.
• What the research proposes: A data-driven domain adaptation approach using MMD and CNNs to bridge the gap between different imaging modalities and improve diagnostic accuracy.
• Results: The method significantly enhanced the model’s ability to generalize across imaging modalities, indicating a potential to improve diagnostic accuracy and efficiency.

Technical Details

Technological frameworks used: Maximum Mean Difference (MMD) with Convolutional Neural Networks (CNNs)

Models used: Custom CNN models designed for domain adaptation

Data used: Public datasets from Kaggle website

Potential Impact

Healthcare providers, medical imaging companies, and AI technology firms in the diagnostic solutions sector.

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