Cited by 392COVID-19 diagnosis using AIRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education

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Chest pathology detection using deep learning with non-medical training

Bar, Yaniv, Diamant, Idit, Wolf, Lior, Lieberman, Sivan, Konen, Eli, Greenspan, Hayit·2015·2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

DOI 10.1109/isbi.2015.7163871

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Abstract

In this work, we examine the strength of deep learning approaches for pathology detection in chest radiographs. Convolutional neural networks (CNN) deep architecture classification approaches have gained popularity due to their ability to learn mid and high level image representations. We explore the ability of CNN learned from a non-medical dataset to identify different types of pathologies in chest x-rays. We tested our algorithm on a 433 image dataset. The best performance was achieved using CNN and GIST features. We obtained an area under curve (AUC) of 0.87-0.94 for the different pathologies. The results demonstrate the feasibility of detecting pathology in chest x-rays using deep learning approaches based on non-medical learning. This is a first-of-its-kind experiment that shows that Deep learning with ImageNet, a large scale non-medical image database may be a good substitute to domain specific representations, which are yet to be available, for general medical image recognition tasks.

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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Oliva, A., & Torralba, A. B. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,321
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451
Visual categorization with bags of keypoints
Csurka, G. (2004) · Cited by 4,379
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W. F., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206848 · Cited by 2,714
Convolutional networks and applications in vision
LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010) · Proceedings of 2010 IEEE International Symposium on Circuits and Systems · doi:10.1109/iscas.2010.5537907 · Cited by 2,213

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Genc, S., Akpınar, K. N., & Karagöl, S. (2020) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora49412.2020.9152607 · Cited by 13
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Authors: Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., & Greenspan, H.
Affiliations: Tel Aviv University (IL)
Sheba Medical Center (IL)
ORCID: Bar, Yaniv
Diamant, Idit
Wolf, Lior
Konen, Eli
Greenspan, Hayit
Venue: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
Publisher: IEEE
Published: 2015-04
Pages: 294-297
DOI: 10.1109/isbi.2015.7163871
OpenAlex: W1570613334
Cited by: 392
References: 16
Citations by year: 2026: 5 · 2025: 9 · 2024: 18 · 2023: 31 · 2022: 38 · 2021: 59 · 2020: 69 · 2019: 55 · 2018: 45 · 2017: 41 · 2016: 19 · 2015: 3
FWCI: 87.84
Type: conference paper
Language: en
Open access: closed
Locations: IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: ImageNet pre-training, transfer learning, convolutional neural networks, GIST features, medical image classification, domain adaptation

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Chest pathology detection using deep learning with non-medical training

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