Abstract
In recent years, computer-assisted diagnostic systems increasingly gained interest through the use of deep learning techniques. Surely, the medical field could be one of the best environments in which the power of the AI algorithms can be tangible for everyone. Deep learning models can be useful to help radiologists elaborate fast and even more accurate diagnosis or accelerate the triage systems in hospitals. However, differently from other fields of works, the collaboration and co-work between data scientists and physicians is crucial in order to achieve better performances. With this work, we show how it is possible to classify X-ray images through a multi-input neural network that also considers clinical data. Indeed, the use of clinical information together with the images allowed us to obtain better results than those already present in the literature on the same data.
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Crobu, F., Di Ciaccio, A. (2021). Deep Learning to Jointly Analyze Images and Clinical Data for Disease Detection. In: Balzano, S., Porzio, G.C., Salvatore, R., Vistocco, D., Vichi, M. (eds) Statistical Learning and Modeling in Data Analysis. CLADAG 2019. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-69944-4_6
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DOI: https://doi.org/10.1007/978-3-030-69944-4_6
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