Poster + Paper
7 April 2023 Cross modal global local representation learning from radiology reports and x-ray chest images
Author Affiliations +
Conference Poster
Abstract
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.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nathan Hadjiyski, Ali Vosoughi, and Axel Wismüller "Cross modal global local representation learning from radiology reports and x-ray chest images", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 1246531 (7 April 2023); https://doi.org/10.1117/12.2654520
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KEYWORDS
Machine learning

Artificial intelligence

Deep learning

Machine vision

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