The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning
Open accessAbstract
Significance: Computed tomography (CT) of the head is the workhorse medical imaging modality used worldwide to diagnose neurologic emergencies. However, these gray scale images are limited by low signal-to-noise, poor contrast, and a high incidence of image artifacts. A unique challenge is to identify tiny subtle abnormalities in a large 3D volume with near-perfect sensitivity. We used a single-stage, end-to-end, fully convolutional neural network to achieve accuracy levels comparable to that of highly trained radiologists, including both identification and localization of abnormalities that are missed by radiologists.
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The pascal visual object classes (VOC) challenge
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Dermatologist-level classification of skin cancer with deep neural networks
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Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639
The ABCs of Measuring Intracerebral Hemorrhage Volumes
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A new classification of head injury based on computerized tomography
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Traumatic brain injury: progress and challenges in prevention, clinical care, and research
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Rauschecker, A. M., Rudie, J. D., Xie, L., Wang, J., Duong, M. T., Botzolakis, E. J., Kovalovich, A. M., Egan, J., Cook, T. C., Bryan, R. N., et al. (2020) · Radiology · doi:10.1148/radiol.2020190283 · Cited by 148
Zhu, S., Gilbert, M., Chetty, I., & Siddiqui, F. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104828 · Cited by 131
Metadata
- Authors
- Kuo, W., Hӓne, C., Mukherjee, P., Malik, J., & Yuh, E. L.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2019-10-21
- Volume
- 116
- Issue
- 45
- Pages
- 22737-22745
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2981941315
- Cited by
- 264
- References
- 22
- FWCI
- 7.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- CoChat. (2026). Citation check: Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning. https://app.cochat.ai/papers/10.1073/pnas.1908021116
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- Kuo, W., Hӓne, C., Mukherjee, P., Malik, J., & Yuh, E. L. (2019). Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1908021116
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