GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays
| العنوان: | GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays |
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| المؤلفون: | Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero, Philip Sellars, Nicolas Papadakis |
| المساهمون: | Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge [UK] (CAM), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), European Project: 777826,NoMADS(2018), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS) |
| المصدر: | Pattern Recognition Pattern Recognition, Elsevier, In press, ⟨10.1016/j.patcog.2021.108274⟩ Pattern Recognition, In press, 122, pp.108274. ⟨10.1016/j.patcog.2021.108274⟩ |
| بيانات النشر: | Elsevier Ltd., 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Computer Science - Machine Learning, Relation (database), Computer science, Computer Vision and Pattern Recognition (cs.CV), Semi-Supervised Learning, Computer Science - Computer Vision and Pattern Recognition, Machine Learning (stat.ML), Semi-supervised learning, Machine learning, computer.software_genre, Article, 030218 nuclear medicine & medical imaging, Machine Learning (cs.LG), 03 medical and health sciences, 0302 clinical medicine, Deep Learning, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Artificial Intelligence, Statistics - Machine Learning, Set (psychology), business.industry, Deep learning, Supervised learning, Chest X-ray, COVID-19, Explainability, Visualization, Identification (information), ComputingMethodologies_PATTERNRECOGNITION, Signal Processing, Graph (abstract data type), Computer Vision and Pattern Recognition, Artificial intelligence, business, computer, 030217 neurology & neurosurgery, Software |
| الوصف: | International audience; Can one learn to diagnose COVID-19 under extreme minimal supervision? Since the outbreak of the novel COVID-19 there has been a rush for developing Artificial Intelligence techniques for expert-level disease identification on Chest X-ray data. In particular, the use of deep supervised learning has become the go-to paradigm. However, the performance of such models is heavily dependent on the availability of a large and representative labelled dataset. The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease. Semi-supervised learning has shown the ability to match the incredible performance of supervised models whilst requiring a small fraction of the labelled examples. This makes the semi-supervised paradigm an attractive option for identifying COVID-19. In this work, we introduce a graph based deep semi-supervised framework for classifying COVID-19 from chest X-rays. Our framework introduces an optimisation model for graph diffusion that reinforces the natural relation among the tiny labelled set and the vast unlabelled data. We then connect the diffusion prediction output as pseudo-labels that are used in an iterative scheme in a deep net. We demonstrate, through our experiments, that our model is able to outperform the current leading supervised model with a tiny fraction of the labelled examples. Finally, we provide attention maps to accommodate the radiologist's mental model, better fitting their perceptual and cognitive abilities. These visualisation aims to assist the radiologist in judging whether the diagnostic is correct or not, and in consequence to accelerate the decision. |
| اللغة: | English |
| تدمد: | 0031-3203 |
| DOI: | 10.1016/j.patcog.2021.108274⟩ |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0709e0d4f21f2dc7f9288b7b909801e4 http://europepmc.org/articles/PMC8387569 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....0709e0d4f21f2dc7f9288b7b909801e4 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 00313203 |
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| DOI: | 10.1016/j.patcog.2021.108274⟩ |