A Multi-modal Deep Learning Method for Classifying Chest Radiology Exams

Non-invasive medical imaging techniques, such as radiography or computed tomography, are extensively used in hospitals and clinics for the diagnosis of diverse injuries or diseases. However, the interpretation of these images, which often results in a free-text radiology report and/or a classification, requires specialized medical professionals, leading to high labor costs and waiting lists. Automatic inference of thoracic diseases from the results of chest radiography exams, e.g. for the purpose of indexing these documents, is still a challenging task, even if combining images with the free-text reports. Deep neural architectures can contribute to a more efficient indexing of radiology exams (e.g., associating the data to diagnostic codes), providing interpretable classification results that can guide the domain experts. This work proposes a novel multi-modal approach, combining a dual path convolutional neural network for processing images with a bidirectional recurrent neural network for processing text, enhanced with attention mechanisms and leveraging pre-trained clinical word embeddings. The experimental results show interesting patterns, e.g. validating the high performance of the individual components, and showing promising results for the multi-modal processing of radiology examination data, particularly when pre-training the components of the model with large pre-existing datasets (i.e., a 10% increase in terms of the average value for the areas under the receiver operating characteristic curves).