In this paper, an innovative multi-modal deep learning model is proposed to
deeply integrate heterogeneous information from medical images and clinical
reports. First, for medical images, convolutional neural networks were used to
extract high-dimensional features and capture key visual information such as
focal details, texture and spatial distribution. Secondly, for clinical report
text, a two-way long and short-term memory network combined with an attention
mechanism is used for deep semantic understanding, and key statements related
to the disease are accurately captured. The two features interact and integrate
effectively through the designed multi-modal fusion layer to realize the joint
representation learning of image and text. In the empirical study, we selected
a large medical image database covering a variety of diseases, combined with
corresponding clinical reports for model training and validation. The proposed
multimodal deep learning model demonstrated substantial superiority in the
realms of disease classification, lesion localization, and clinical description
generation, as evidenced by the experimental results.

通过创新的多模态深度学习模型，本文旨在深度整合医学图像和临床报告等异构信息。使用卷积神经网络提取高维特征，捕捉关键视觉信息；利用双向长短时记忆网络结合注意机制，在临床报告文本中进行深度语义理解，准确捕捉与疾病相关的关键陈述。通过设计的多模态融合层，有效实现图像和文本的联合表征学习。实证研究中，利用大型医学图像数据库和相应的临床报告进行模型训练和验证，实验结果明显展示了多模态深度学习模型在疾病分类、损伤定位和临床描述生成方面的显著优越性。