References
Agarwal S, Wood D, Grzeda M, Suresh C, Din M, Cole J, Modat M, Booth TC. Systematic Review of Artificial Intelligence for Abnormality Detection in High-volume Neuroimaging and Subgroup Meta-analysis for Intracranial Hemorrhage Detection. Clin Neuroradiol. 2023 Dec;33(4):943-956. doi: 10.1007/s00062-023-01291-1. Epub 2023 Jun 1. PMID: 37261453; PMCID: PMC10233528.
Agarwal, Siddharth, David Wood, Mariusz Grzeda, Chandhini Suresh, Munaib Din, James Cole, Marc Modat, and Thomas C. Booth. ”Systematic Review of Artificial Intelligence for Abnormality Detection in High-volume Neuroimaging and Subgroup Meta-analysis for Intracranial Hemorrhage Detection.” Clinical Neuroradiology  (2023): 1-14.
Alsentzer, E., Murphy, J. R., Boag, W., Weng, W. H., Jin, D., Naumann, T., & McDermott, M. (2019). Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323 .
Booth TC, Williams M, Luis A, Cardoso J, Ashkan K, Shuaib H. Machine learning and glioma imaging biomarkers. Clin Radiol. 2020 Jan;75(1):20-32. doi: 10.1016/j.crad.2019.07.001. Epub 2019 Jul 29. PMID: 31371027; PMCID: PMC6927796.
Cai, J., Tang, Y., Lu, L., Harrison, A. P., Yan, K., Xiao, J., … & Summers, R. M. (2018). Accurate weakly-supervised deep lesion segmentation using large-scale clinical annotations: Slice-propagated 3d mask generation from 2d recist. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part IV 11  (pp. 396-404). Springer International Publishing.
Cole, J., Wood, D., & Booth, T. (2020). Visual attention as a model for interpretable neuroimage classification in dementia: Doctor AI: Making computers explain their decisions. Alzheimer’s & Dementia16 , e037351.
Cole, J., Wood, D. and Booth, T. (2020), Visual attention as a model for interpretable neuroimage classification in dementia. Alzheimer’s Dement., 16: e037351. https://doi.org/10.1002/alz.037351
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition  (pp. 248-255). Ieee.
Din M, Agarwal S, Grzeda M, et al. Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis. Journal of NeuroInterventional Surgery  2023;15: 262-271.
Din, M., Agarwal, S., Grzeda, M., Wood, D. A., Modat, M., & Booth, T. C. (2023). Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis. Journal of NeuroInterventional Surgery15 (3), 262-271.
Feng, X., Yang, J., Laine, A. F., & Angelini, E. D. (2017). Discriminative localization in CNNs for weakly-supervised segmentation of pulmonary nodules. In Medical Image Computing and Computer Assisted Intervention− MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20  (pp. 568-576). Springer International Publishing.
Izadyyazdanabadi, M., Belykh, E., Mooney, M., Martirosyan, N., Eschbacher, J., Nakaji, P., … & Yang, Y. (2018). Convolutional neural networks: Ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical CLE images. Journal of Visual Communication and Image Representation54 , 10-20.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 .
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics36 (4), 1234-1240.
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., … & Van Leemput, K. (2014). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging34 (10), 1993-2024.
NHS. Operational Information for Commissioning NHS England 21st February 2019, accessed from https://www.england.nhs.uk/statistics/wp- content/uploads/sites/2/2019/03/Provisional-Monthly-Diagnostic-Imaging-Dataset- Statistics-2019-03-21-1.pdf. Cambridge University Press, 2019
Poudel, R. P., Lamata, P., & Montana, G. (2017). Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation. In Reconstruction, Segmentation, and Analysis of Medical Images: First International Workshops, RAMBO 2016 and HVSMR 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers 1  (pp. 83-94). Springer International Publishing.
Wei, Y., Liang, X., Chen, Y., Shen, X., Cheng, M. M., Feng, J., … & Yan, S. (2016). Stc: A simple to complex framework for weakly-supervised semantic segmentation. IEEE transactions on pattern analysis and machine intelligence39 (11), 2314-2320.
Winzeck, S., Hakim, A., McKinley, R., Pinto, J. A., Alves, V., Silva, C., … & Reyes, M. (2018). ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Frontiers in neurology9 , 679.
Wood, D., Cole, J., & Booth, T. (2019). NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification. arXiv preprint arXiv:1910.04721 .
Wood, David A., et al. ”Labelling imaging datasets on the basis of neuroradiology reports: a validation study.” arXiv preprint arXiv:2007.04226  (2020).
Wood, D.A. et al.  (2020). Labelling Imaging Datasets on the Basis of Neuroradiology Reports: A Validation Study. In: Cardoso, J., et al.  Interpretable and Annotation-Efficient Learning for Medical Image Computing. IMIMIC MIL3ID LABELS 2020 2020 2020. Lecture Notes in Computer Science, vol 12446. Springer, Cham. https://doi.org/10.1007/978-3-030-61166-8_27
Wood, D. A., Kafiabadi, S., Al Busaidi, A., Guilhem, E., Lynch, J., Townend, M., … & Booth, T. C. (2020, October). Labelling Imaging Datasets on the Basis of Neuroradiology Reports: A Validation Study. In International Workshop on Interpretability of Machine Intelligence in Medical Image Computing  (pp. 254-265).
Wood, D. A., Kafiabadi, S., Al Busaidi, A., Guilhem, E., Lynch, J., Townend, M., … & Booth, T. C. (2020). Labelling imaging datasets on the basis of neuroradiology reports: a validation study. In Interpretable and Annotation-Efficient Learning for Medical Image Computing: Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings 3  (pp. 254-265). Springer International Publishing.
Wood, D. A., Lynch, J., Kafiabadi, S., Guilhem, E., Al Busaidi, A., Montvila, A., … & Booth, T. C. (2020, September). Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM). In Medical Imaging with Deep Learning  (pp. 811-826). PMLR.
Wood, D.A., Lynch, J., Kafiabadi, S., Guilhem, E., Al Busaidi, A., Montvila, A., Varsavsky, T., Siddiqui, J., Gadapa, N., Townend, M., Kiik, M., Patel, K., Barker, G., Ourselin, S., Cole, J.H. & Booth, T.C.. (2020). Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM). Proceedings of the Third Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research121:811-826 Available from https://proceedings.mlr.press/v121/wood20a.html.
Wood, David A., et al. ”Automated triaging of head MRI examinations using convolutional neural networks.” Medical Imaging with Deep Learning . PMLR, 2021.
Wood, D. A., Kafiabadi, S., Al Busaidi, A., Guilhem, E., Montvila, A., Agarwal, S., … & Booth, T. C. (2021, August). Automated triaging of head MRI examinations using convolutional neural networks. In Medical Imaging with Deep Learning  (pp. 813-841). PMLR.
Wood, D.A., Kafiabadi, S., Al Busaidi, A. et al.  Deep learning to automate the labelling of head MRI datasets for computer vision applications. Eur Radiol  32, 725–736 (2022). https://doi.org/10.1007/s00330-021-08132-0
Wood, D. A., Kafiabadi, S., Al Busaidi, A., Guilhem, E., Montvila, A., Lynch, J., … & Booth, T. C. (2022). Deep learning models for triaging hospital head MRI examinations. Medical Image Analysis78 , 102391.
Wood, David A., et al. ”Deep learning models for triaging hospital head MRI examinations.” Medical Image Analysis  78 (2022): 102391.
Wood DA, Kafiabadi S, Al Busaidi A, Guilhem E, Montvila A, Lynch J, et al. Deep learning models for triaging hospital head MRI examinations. Med Image Anal . (2022) 78:102391. doi: 10.1016/j.media.2022.102391
Wu, K., Du, B., Luo, M., Wen, H., Shen, Y., & Feng, J. (2019). Weakly supervised brain lesion segmentation via attentional representation learning. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22  (pp. 211-219). Springer International Publishing.
Yan, Y., Kawahara, J., & Hamarneh, G. (2019). Melanoma recognition via visual attention. In Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings 26  (pp. 793-804). Springer International Publishing.
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016, June). Hierarchical attention networks for document classification. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies  (pp. 1480-1489).
Zhang, Z., Xie, Y., Xing, F., McGough, M., & Yang, L. (2017). Mdnet: A semantically and visually interpretable medical image diagnosis network. In Proceedings of the IEEE conference on computer vision and pattern recognition  (pp. 6428-6436).
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition  (pp. 2921-2929).