Cross-Modal Domain Adaptation in Brain Disease Diagnosis: Maximum Mean Discrepancy-based Convolutional Neural Networks

TL;DR

This paper proposes a novel cross-modal domain adaptation approach using MMD-based CNNs to improve brain disease diagnosis accuracy across different medical imaging modalities, addressing data scarcity issues.

Contribution

It introduces a MMD-based domain adaptation framework combined with CNNs for cross-modality medical image analysis, enhancing diagnostic performance.

Findings

Improved diagnostic accuracy across MRI and CT images.

Effective reduction of domain differences using MMD.

Potential for better clinical decision support in resource-limited settings.

Abstract

Brain disorders are a major challenge to global health, causing millions of deaths each year. Accurate diagnosis of these diseases relies heavily on advanced medical imaging techniques such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, the scarcity of annotated data poses a significant challenge in deploying machine learning models for medical diagnosis. To address this limitation, deep learning techniques have shown considerable promise. Domain adaptation techniques enhance a model's ability to generalize across imaging modalities by transferring knowledge from one domain (e.g., CT images) to another (e.g., MRI images). Such cross-modality adaptation is essential to improve the ability of models to consistently generalize across different imaging modalities. This study collected relevant resources from the Kaggle website and employed the Maximum Mean Difference (MMD) method - a popular domain adaptation method - to reduce the differences between imaging domains. By combining MMD with Convolutional Neural Networks (CNNs), the accuracy and utility of the model is obviously enhanced. The excellent experimental results highlight the great potential of data-driven domain adaptation techniques to improve diagnostic accuracy and efficiency, especially in resource-limited environments. By bridging the gap between different imaging modalities, the study aims to provide clinicians with more reliable diagnostic tools.