Deep learning models can be applied successfully in real-work problems; however, training most of these models requires massive data. Recent methods use language and vision, but unfortunately, they rely on datasets that are not usually publicly available. Here we pave the way for further research in the multimodal language-vision domain for radiology. In this paper, we train a representation learning method that uses local and global representations of the language and vision through an attention mechanism and based on the publicly available Indiana University Radiology Report (IU-RR) dataset. Furthermore, we use the learned representations to diagnose five lung pathologies: atelectasis, cardiomegaly, edema, pleural effusion, and consolidation. Finally, we use both supervised and zero-shot classifications to extensively analyze the performance of the representation learning on the IU-RR dataset. Average Area Under the Curve (AUC) is used to evaluate the accuracy of the classifiers for classifying the five lung pathologies. The average AUC for classifying the five lung pathologies on the IU-RR test set ranged from 0.85 to 0.87 using the different training datasets, namely CheXpert and CheXphoto. These results compare favorably to other studies using UI-RR. Extensive experiments confirm consistent results for classifying lung pathologies using the multimodal global local representations of language and vision information.

本文使用公開的印第安納大學放射學報告 (IU-RR) 資料集，訓練具有全局和局部的語言和視覺元素的多模式表示學習方法，進而診斷五種肺病理學，包括肺不脹、心臟肥大、水腫、胸膜積液和實質密度增加。使用CheXpert和CheXphoto這兩種不同的訓練資料集，平均AUC在0.85至0.87之間，這些結果與使用UI-RR的其他研究相比也相當有競爭力。

从放射学报告和X光胸片图像中进行跨模态全局局部表示学习