【講題】Applications of Deep Learning in Medical Image Analysis and Healthcare Information Integration

【演講時間】4月18日(星期五)下午1點30分 

【演講地點】清華大學校本部第二綜合大樓B側8樓A813室

【摘要】

Deep learning has emerged as a transformative technology in medical imaging, offering advanced capabilities in representation learning and pattern recognition. This talk introduces the fundamentals of Convolutional Neural Networks (CNNs), emphasizing their mathematical structure and effectiveness in processing high-dimensional medical images. Key imaging modalities—such as MRI, CT, X-rays, and color fundus photography—will be discussed in the context of clinical diagnosis and biomedical research. We will explore the application of deep learning to medical image classification, object detection, and semantic segmentation. These models enable automated disease identification, lesion localization, and pixel-level annotation of anatomical structures, contributing to more accurate and efficient clinical workflows. The talk will also highlight the potential of Retrieval-Augmented Generation (RAG), a hybrid architecture that combines generative language models with external knowledge retrieval. RAG enhances information integration in medical contexts, such as clinical summarization and decision support, mainly when dealing with limited training data or complex, context-sensitive queries.