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Deep learning for understanding multilabel imbalanced Chest X-ray datasets

dc.contributor.authorLiz, Helena
dc.contributor.authorHuertas-Tato, Javier
dc.contributor.authorSánchez-Montañés, Manuel
dc.contributor.authorSer, Javier del
dc.contributor.authorCamacho, David
dc.identifier.citationHelena Liz, Javier Huertas-Tato, Manuel Sánchez-Montañés, Javier Del Ser, David Camacho, Deep learning for understanding multilabel imbalanced Chest X-ray datasets, Future Generation Computer Systems, Volume 144, 2023, Pages 291-306, ISSN 0167-739X,
dc.descriptionThis work has been funded by Grant PLEC2021-007681 (XAI-DisInfodemics) and PID2020-117263GB-100 (FightDIS) funded by MCIN/AEI/ 10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union NextGenerationEU/PRTR”, by the research project CIVIC: Intelligent characterisation of the veracity of the information related to COVID-19, granted by BBVA FOUNDATION GRANTS FOR SCIENTIFIC RESEARCH TEAMS SARS-CoV-2 and COVID-19, by European Comission under IBERIFIER - Iberian Digital Media Research and Fact-Checking Hub (2020-EU-IA-0252), by “Convenio Plurianual with the Universidad Politécnica de Madrid in the actuation line of Programa de Excelencia para el Profesorado Universitario”, and by Comunidad Autónoma de Madrid under S2018/TCS-4566 (CYNAMON) grant. M. Sánchez-Montañés has been supported by grants PID2021-127946OB-I00 and PID2021-122347NB-I00 (funded by MCIN/AEI/ 10.13039/501100011033 and ERDF - “A way of making Europe”) and Comunidad Autónoma de Madrid, Spain (S2017/BMD-3688 MULTI-TARGET&VIEW-CM grant). J. Del Ser thanks the financial support of the Spanish Centro para el Desarrollo Tecnológico Industrial (CDTI, Ministry of Science and Innovation) through the “Red Cervera” Programme (AI4ES project), as well as the support of the Basque Government (consolidated research group MATHMODE, ref. IT1456-22)es
dc.description.abstractOver the last few years, convolutional neural networks (CNNs) have dominated the field of computer vision thanks to their ability to extract features and their outstanding performance in classification problems, for example in the automatic analysis of X-rays. Unfortunately, these neural networks are considered black-box algorithms, i.e. it is impossible to understand how the algorithm has achieved the final result. To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques. Most of the work in the medical field focuses on binary or multiclass classification problems. However, in many real-life situations, such as chest X-rays, radiological signs of different diseases can appear at the same time. This gives rise to what is known as "multilabel classification problems". A disadvantage of these tasks is class imbalance, i.e. different labels do not have the same number of samples. The main contribution of this paper is a Deep Learning methodology for imbalanced, multilabel chest X-ray datasets. It establishes a baseline for the currently underutilised PadChest dataset and a new eXplainable AI technique based on heatmaps. This technique also includes probabilities and inter-model matching. The results of our system are promising, especially considering the number of labels used. Furthermore, the heatmaps match the expected areas, i.e. they mark the areas that an expert would use to make a
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.subjectConvolutional neural networkses
dc.subjectChest X-rayses
dc.subjectExplainable AIes
dc.subjectEnsemble Methodologyes
dc.titleDeep learning for understanding multilabel imbalanced Chest X-ray datasetses

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