Leveraging clinical indications and demographics to improve multilabel abnormality classification in 3D chest CT scans

The increasing number of Computed Tomography (CT) scan examinations coupled with the time-intensive nature of manual analysis highlights the need for efficient automated analysis methods to assist radiologists in detecting anomalies. While state-of-the-art deep learning approaches directly classifies abnormalities from three-dimensional (3D) CT images, radiologists also rely on clinical indications and patient demographics such as age and sex for diagnosis. We propose a multimodal model that integrates 3D chest CT scans, clinical information reports, patient age and sex to improve multi-label abnormality classification. Our approach leverages both imaging and non-imaging data by combining visual and textual neural networks. We extend our work by conducting an ablation study on a public dataset to evaluate and demonstrate the efficacy of our approach.