The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning
Abstract
Automated Screening of COVID-19 from chest CT is of emergency and importance during the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 is still a massive challenge due to the spatial complexity of 3D volumes, the labeling difficulty of infection areas, and the slight discrepancy between COVID-19 and other viral pneumonia in chest CT. While a few pioneering works have made significant progress, they are either demanding manual annotations of infection areas or lack of interpretability. In this paper, we report our attempt towards achieving highly accurate and interpretable screening of COVID-19 from chest CT with weak labels. We propose an attention-based deep 3D multiple instance learning (AD3D-MIL) where a patient-level label is assigned to a 3D chest CT that is viewed as a bag of instances. AD3D-MIL can semantically generate deep 3D instances following the possible infection area. AD3D-MIL further applies an attention-based pooling approach to 3D instances to provide insight into each instance's contribution to the bag label. AD3D-MIL finally learns Bernoulli distributions of the bag-level labels for more accessible learning. We collected 460 chest CT examples: 230 CT examples from 79 patients with COVID-19, 100 CT examples from 100 patients with common pneumonia, and 130 CT examples from 130 people without pneumonia. A series of empirical studies show that our algorithm achieves an overall accuracy of 97.9%, AUC of 99.0%, and Cohen kappa score of 95.7%. These advantages endow our algorithm as an efficient assisted tool in the screening of COVID-19.
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Cites
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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A Structured Self-attentive Sentence Embedding
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“are beneficial for MIL classifier [23].”
Cited by
Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00307-0 · Cited by 1,044
Self-citationRoberts, M. T., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Avilés-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2020) · Apollo (University of Cambridge) · doi:10.17863/cam.64000 · Cited by 895
Self-citationAI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app
Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, M. S., Ali, K., John, C. N., Hussain, M. I., & Nabeel, M. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100378 · Cited by 621
Applications of artificial intelligence in battling against covid-19: A literature review
Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210
Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review
de Vries, B. M., Zwezerijnen, G. J. C., Burchell, G. L., van Velden, F. H. P., Menke-van der Houven van Oordt, C. W., & Boellaard, R. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1180773 · Cited by 106
Metadata
- Authors
- Han, Z., Wei, B., Hong, Y., Li, T., Cong, J., Zhu, X., Wei, H., & Zhang, W.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2020-08
- Volume
- 39
- Issue
- 8
- Pages
- 2584-2594
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W3026931681
- Cited by
- 325
- References
- 10
- FWCI
- 28.24
- Type
- journal article
- Language
- en
- Open access
- closed
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- Han, Z., Wei, B., Hong, Y., Li, T., Cong, J., Zhu, X., Wei, H., & Zhang, W. (2020). Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2020.2996256
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