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.

翻译：脑部疾病是全球健康面临的重大挑战，每年导致数百万人死亡。这些疾病的准确诊断高度依赖磁共振成像（MRI）和计算机断层扫描（CT）等先进医学影像技术。然而，标注数据的稀缺性对部署机器学习模型进行医学诊断构成了重大挑战。为应对这一局限，深度学习技术展现出显著潜力。域适应技术通过将知识从一个域（如CT图像）迁移到另一个域（如MRI图像），增强了模型跨成像模态的泛化能力。这种跨模态适应对于提升模型在不同成像模态间的一致泛化能力至关重要。本研究从Kaggle网站收集相关资源，采用最大均值差异（MMD）这一主流域适应方法以降低成像域间的差异。通过将MMD与卷积神经网络（CNN）相结合，模型的准确性和实用性得到显著提升。出色的实验结果凸显了数据驱动型域适应技术在提升诊断准确性与效率方面的巨大潜力，尤其是在资源受限的环境中。本研究旨在通过弥合不同成像模态间的差异，为临床医生提供更可靠的诊断工具。