Fadi Dornaika; Sally El Hajjar; Jinan Charafeddine
Towards unsupervised radiograph clustering for COVID-19: The use of graph-based multi-view clustering Article de journal
Dans: Engineering Applications Of Artificial Intelligence, vol. 133, no. Part D, p. 108336, 2024.
@article{dornaika_2943,
title = {Towards unsupervised radiograph clustering for COVID-19: The use of graph-based multi-view clustering},
author = {Fadi Dornaika and Sally El Hajjar and Jinan Charafeddine},
url = {https://www.sciencedirect.com/science/article/pii/S0952197624004949?via%3Dihub},
year = {2024},
date = {2024-07-01},
journal = {Engineering Applications Of Artificial Intelligence},
volume = {133},
number = {Part D},
pages = {108336},
abstract = {Automatic classification methods widely used for diagnosing and analyzing COVID-19 cases. These methods assume known labels and rely on a single view of the dataset. Given the prevalence of COVID-19 cases and the extensive volume of patient records lacking labels, this communication underscores our unique approach?conducting the first study on COVID-19 case diagnosis in an unsupervised manner. Our work operates under the assumption of prior knowledge regarding the number of classes, such as COVID-19, pneumonia, and normal, in a case study.
By adopting an unsupervised learning paradigm, we leverage the wealth of unlabeled data, reducing dependence on human experts for annotating numerous images. This paper introduces an enhanced version of a recent direct method where non-negative cluster indices and spectral embeddings are jointly estimated. Beyond the inherent advantages of this method, our proposed model introduces improvements through two additional types of constraints: (i) ensuring consistent smoothing of cluster labels across all views and (ii) imposing an orthogonality constraint on the matrix of cluster assignments. The efficacy of the proposed method is demonstrated using the public COVIDx dataset with three classes, showcasing promising results in categorizing radiographs. The proposed approach is tested on other public image datasets to assess its effectiveness.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Fadi Dornaika; Jinan Charafeddine
Semi-supervised Classification through Data and Label Graph Fusion Proceedings Article
Dans: "2023 International Conference on Computer and Applications (ICCA)", p. pp. 1-6, IEEE, Cairo , Egypt, 2023, ISBN: ISBN 979-8-3503-0325-4.
@inproceedings{dornaika_2957,
title = {Semi-supervised Classification through Data and Label Graph Fusion},
author = {Fadi Dornaika and Jinan Charafeddine},
url = {https://ieeexplore.ieee.org/abstract/document/10401729},
issn = {ISBN 979-8-3503-0325-4},
year = {2023},
date = {2023-12-01},
booktitle = {"2023 International Conference on Computer and Applications (ICCA)"},
pages = {pp. 1-6},
publisher = {IEEE},
address = {Cairo , Egypt},
abstract = {This study introduces a groundbreaking structure for semi-supervised learning based on graphs. Our technique provides an all-encompassing strategy that simultaneously tackles the challenges of label prediction and linear transformation. Specifically, the linear transformation we advocate is designed to forge a distinguishing subspace, thereby significantly compressing the data's dimensionality.In advancing semi-supervised learning techniques, our framework particularly focuses on effectively utilizing the intrinsic data configuration and the provisional labels related to the unlabeled examples in our possession. This distinctive methodology leads to a more sophisticated and discriminative form of linear transformation. Tests carried out on authentic image datasets clearly validate the efficiency of the method we advocate. These tests repeatedly show enhanced performance in contrast to semi-supervised strategies that address the fusion of data and label deduction in isolation.
keywords: {Estimation;Semisupervised learning;Iterative methods;Labeling;Graph-based semi-supervised learning;data graph;label graph;graph fusion;pattern recognition},
URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10401729&isnumber=10401330},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
No posts by this author.
N'hésitez pas à contacter le service des admissions pour tout renseignement complémentaire :