Open Access
| Issue |
BIO Web Conf.
Volume 204, 2025
International Conference on Advancing Science and Technologies in Health Science (IEM-HEALS 2025)
|
|
|---|---|---|
| Article Number | 01024 | |
| Number of page(s) | 11 | |
| DOI | https://doi.org/10.1051/bioconf/202520401024 | |
| Published online | 12 December 2025 | |
- J. T. Senders, O. Arnaout, A. V. Karhade, H. H. Hassebrock, W. B. Gormley, M. L. Broekman, and T. R. Smith, “Natural and artificial intelligence in neurosurgery: A systematic review,” Neurosurgery 83, 181–192 (2018). [Google Scholar]
- Z. Liu, S. Wang, J. W. Di Dong, C. Fang, X. Zhou, K. Sun, L. Li, B. Li, M. Wang, and J. Tian, “The applications of radiomics in precision diagnosis and treatment of oncology: Opportunities and challenges,” Theranostics 9, 1303 (2019). [Google Scholar]
- M. Sailer, W. H. van Zwam, J. E. Wildberger, and J. P. Grutters, Cost effectiveness modelling in diagnostic imaging: A stepwise approach, Eur. Radiol. 25, 3629–3637 (2015). [Google Scholar]
- P. Brady, “Error and discrepancy in radiology: Inevitable or avoidable?” Insights Imaging 8, 171–182 (2017). [Google Scholar]
- K. Sakai and K. Yamada, “Machine learning studies on major brain diseases: 5-year trends of 2014–2018,” Jpn. J. Radiol. 37, 34–72 (2019). [Google Scholar]
- Z. Akkus, A. Galimzianova, A. Hoogi, D. L. Rubin, and B. J. Erickson, “Deep learning for brain MRI segmentation: State of the art and future directions,” J. Digital Imaging 30, 449–459 (2017). [Google Scholar]
- Gudigar, U. Raghavendra, A. Hegde, M. Kalyani, E. J. Ciaccio, and U. R. Acharya, “Brain pathology identification using computer aided diagnostic tool: A systematic review,” Comput. Methods Programs Biomed. 187, 105205 (2020) [Google Scholar]
- A.-A. Valliani, D. Ranti, and E. K. Oermann, “Deep learning and neurology:A systematic review,” Neurol. Ther. 8, 351–315 (2019). [Google Scholar]
- M. P. McBee, O. A. Awan, A. T. Colucci, C. W. Ghobadi, N. Kadom, A. P. Kansagra, S. Tridandapani, and W. F. Auffermann, “Deep learning in radiology,” Acad. Radiol. 25, 1472–1480 (2018). [Google Scholar]
- Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT Press, 2016). [Google Scholar]
- G. R. Salama, L. A. Heier, P. Patel, R. Ramakrishna, R. Magge, and A. J. Tsiouris, “Diffusion weighted/tensor imaging, functional MRI and perfusion weighted imaging in glioblastoma-foundations and future,” Front. Neurol. 8, 660 (2018). [Google Scholar]
- A. Marzullo, G. Kocevar, C. Stamile, F. Durand-Dubief, G. Terracina, F. Calimeri, and D. Sappey-Marinier, “Classification of multiple sclerosis clinical profiles via graph convolutional neural networks,” Front. Neurosci. 13, 594 (2019). [Google Scholar]
- T. Senders, M. M. Zaki, A. V. Karhade, B. Chang, W. B. Gormley, M. L. Broekman, T. R. Smith, and O. Arnaout, “An introduction and overview of machine learning in neurosurgical care,” Acta Neurochir. 160, 29–38 (2018) [Google Scholar]
- H. Lee, S. Yune, M. Mansouri, M. Kim, S. H. Tajmir, C. E. Guerrier, S. A. Ebert, S. R. Pomerantz, J. M. Romero, S. Kamalian, R. G. Gonzalez, M. H. Lev, and S. Do, “An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets,” Nat. Biomed. Eng. 3, 173–182 (2019) [Google Scholar]
- V. Cherukuri, P. Ssenyonga, B. C. Warf, A. V. Kulkarni, V. Monga, and S. J. Schiff, “Learning based segmentation of CT brain images: Application to postoperative hydrocephalic scans,” IEEE Trans. Biomed. Eng. 65, 1871–1884 (2017). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.