AI Scientific Evidence - Scientific publication highlights and references
We understand the importance of providing you with evidence-based solutions and are pleased to provide you with this overview of the scientific evidence on our AI solutions.
CT
MRI
Healthcare IT
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Canon Medical scientific list with AI technologies
Technical references
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Á. V. Juanco Müller, J. F. C. Mota, C. Hoogendoorn
Segmentation of Skin Lesions by Superpixel Classification with Graph-Context CNN MIUA 2021 |
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| Schrempf, P., Watson, H., Park, E., Pajak, M., MacKinnon, H., Muir, K.W., Harris-Birtill, D. and O’Neil, A.Q. (2021). Templated text synthesis for expert-guided multi-label extraction from radiology reports. Machine Learning and Knowledge Extraction, 3(2), pp.299-317. | Read article |
| Anderson, O., Kidd, A.C., Goatman, K.A., Weir, A.J., Voisey, J., Dilys, V., Siebert, J.P., and Blyth, K.G. (2020). Fully automated volumetric measurement of malignant pleural mesothelioma from computed tomography images by deep learning: Preliminary results of an internal validation. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 2: BIOIMAGING, pp. 64–73, SCITEPRESS. | Read article |
| Elskhawy, A., Lisowska, A., Keicher, M., Henry, J., Thomson, P. and Navab, N. (2020). Continual Class Incremental Learning for CT Thoracic Segmentation. In MICCAI workshop on “Domain Adaptation and Representation Transfer” (DART). | Read article |
| Jacenków, G., O’Neil, A.Q., Mohr, B. and Tsaftaris, S.A. (2020). INSIDE: Steering Spatial Attention with Non-imaging Information in CNNs. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 385-395). Springer, Cham. | Read article |
| Liu, X., Thermos, S., Chartsias, A., O'Neil, A. and Tsaftaris, S.A. (2021). Disentangled Representations for Domain-Generalized Cardiac Segmentation. In MICCAI workshop on “Statistical Atlases and Computational Models of the Heart” (STACOM), submission to the Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation (M&Ms) Challenge. | Read article |
| Schrempf, P., Watson, H., Mikhael, S., Pajak, M., Falis, M., Lisowska, A., Muir, K.W., Harris-Birtill, D. and O’Neil, A.Q. (2020). Paying Per-Label Attention for Multi-label Extraction from Radiology Reports. In MICCAI workshop on “Large-scale Annotation of Biomedical Data and Expert Label Synthesis” (LABELS). | Read article |
| Shaw, S., Pajak, M., Lisowska, A., Tsaftaris, S.A., O’Neil, A.Q., (2020). Teacher-Student chain for efficient semi-supervised histology image classification. In ICLR workshop on “AI for Affordable Healthcare” (AI4AH). | Read article |
| Appelgren, M., Schrempf, P., Falis, M., Ikeda, S. and O'Neil, A.Q., (2019). Language Transfer for Early Warning of Epidemics from Social Media. In NeurIPS workshop on “Artificial Intelligence for Humanitarian Assistance and Disaster Response” (AI+HADR). | Read article |
| Falis, M., Pajak, M., Lisowska, A., Schrempf, P., Deckers, L., Mikhael, S., Tsaftaris, S. and O’Neil, A. Q., (2019). Ontological attention ensembles for capturing semantic concepts in ICD code prediction from clinical text. In EM NLP Workshop “Tenth International Workshop on Health Text Mining and Information Analysis” (LOUHI), p. 168-177. | Read article |
| Jacenków, G., Chartsias, A., Mohr, B. and Tsaftaris, S.A. (2019). Conditioning Convolutional Segmentation Architectures with Non-Imaging Data. Medical Imaging with Deep Learning (Extended Abstract Track). | Download PDF |
| Zotova, D., Lisowska, A., Anderson, O., Dilys, V. and O’Neil, A. Q., (2019). Comparison of active learning strategies applied to lung nodule segmentation in CT scans. In MICCAI workshop on “Large-scale Annotation of Biomedical Data and Expert Label Synthesis” (LABELS). | Read article |
| Daykin, M., Sellathurai, M. and Poole, I., 2018, July. A Comparison of Unsupervised Abnormality Detection Methods for Interstitial Lung Disease . In Annual Conference on Medical Image Understanding and Analysis (pp. 287-298). Springer, Cham. | Download PDF |
| Lisowska, A., O'Neil, A. and Poole, I. (2018) Cross-cohort Evaluation of Machine Learning Approaches to Fall Detection from Accelerometer Data . In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, January 2018, ISBN 978-989-758-281-3, pages 77-82. | Read article |
| O'Neil, A.Q., Kascenas, A., Henry, J., Wyeth, D., Shepherd, M., Beveridge, E., Clunie, L., Sansom, C., Šeduikytė, E., Muir, K. and Poole, I., (2018). Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3D CT data . ECCV 2018 Workshop "Geometry Meets Deep Learning". | Read article |
| Sloan, J., Goatman, K. and Siebert, J. (2018). Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration . In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, ISBN 978-989-758-278-3, pages 89-99. | Read article |
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Healthcare Information Technology
| Rava et al. | Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage | World Neurosurgery (2021) | Read article |
| Rava et al. | Validation of an Artificial Intelligence Driven Large Vessel Occlusion Detection Algorithm for Acute Ischemic Stroke Patients | The Neuroradiology Journal (2021) | Read article |
| Rava et al. | Assessment of a Bayesian Vitrea CT Perfusion Analysis to Predict Final Infarct and Penumbra Volumes in Patients with Acute Ischemic Stroke: A Comparison with RAPID | American Journal of Neuroradiology (AJNR) (2020) | Read article |
| Rava et al. | Assessment of computed tomography perfusion software in predicting spatial location and volume of infarct in acute ischemic stroke patients: a comparison of Sphere, Vitrea, and RAPID | Journal of NeuroInterventional Surgery (JNIS) (2020) | Read article |
| Rava et al. | Enhancing performance of a computed tomography perfusion software for improved prediction of final infarct volume in acute ischemic stroke patients | The Neuroradiology Journal (2021) | Read article |
| Ohno et al. | Machine learning for lung CT texture analysis: Improvement of inter-observer agreement for radiological finding classification in patients with pulmonary diseases | Eur J Radiol. (2021) | Read article |
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MRI
| Sagawa et al. | Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics| Magn Reson Med Sci (2020) | Read article |
| Kidoh et al. | Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers | Magn Reson Med Sci (2020) | Read article |
| Yokota et al. | Effects of Deep Learning Reconstruction Technique in High-Resolution Non-contrast Magnetic Resonance Coronary Angiography at a 3-Tesla Machine | Can Assoc Radiol J (2021) | Read article |
| Mori et al. | Ultrashort echo time time-spatial labeling inversion pulse magnetic resonance angiography with denoising deep learning reconstruction for the assessment of abdominal visceral arteries | J Magn Reson Imaging (2021) | Read article |
| Ueda et al. | Compressed sensing and deep learning reconstruction for women's pelvic MRI denoising: Utility for improving image quality and examination time in routine clinical practice | Eur J Radiol (2021) | Read article |
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X-Ray
| Brestel et al. | RadBot-CXR: Classification of Four Clinical Finding Categories in Chest X-Ray Using Deep Learning | Medicine (2018) | Download PDF |
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CT
| Nakamura et al. | Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT | European Radiology | (2021) | Read article |
| Matsukiyo et al. | Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions | Jpn J Radiol | (2021) | Read article |
| Greffier et al. | Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study | Medical Physics | (2021) | Read article |
| Greffier at al. | Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data | Diagn Interv Imaging | (2021) | Read article |
| Brady et al. | Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction | Radiology | (2021) | Read article |
| McLeavy et al. | The future of CT: deep learning reconstruction | Clin Radiol. (2021) | Read article |
| Matsuura et al. | Feature-Aware Deep-Learning Reconstruction for Context-Sensitive X-ray Computed Tomography | IEEE (2021) | Read article |
| Bernard et al. | Deep learning reconstruction versus iterative reconstruction for cardiac CT angiography in a stroke imaging protocol: reduced radiation dose and improved image quality. | Quant Imaging Med Surg. (2021) | Read article |
| Higaki et al. | Deep Learning Reconstruction at CT: Phantom Study of the Image Characteristics | Academic Radiology | (2020) | Read article |
| Akagi et al. | Deep learning reconstruction of equilibrium phase CT images in obese patients | Eur J Radiol | (2020) | Read article |
| Singh et al. | Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT | AJR Am J Roentgenol | (2020) | Read article |
| Lenfant et al. | Deep Learning Versus Iterative Reconstruction for CT Pulmonary Angiography in the Emergency Setting: Improved Image Quality and Reduced Radiation Dose | Diagnostics (2020) | Read article |
| Nakamura et al.| Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality | J Comput Assist Tomogr (2019) | Read article |
| Higaki et al. | Improvement of image quality at CT and MRI using deep learning | Japanese Journal of Radiology (2019) | Read article |
| Nakamura et al. | Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT | Eur Radiol (2021) | Read article |
| Urikura et al. | Deep learning-based reconstruction in ultra-high-resolution computed tomography: Can image noise caused by high definition detector and the miniaturization of matrix element size be improved? | Phys Med. (2021) | Read article |
| Matsukiyo et al. | Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions | Jpn J Radiol (2021) | Read article |
| Tamura et al. | Superior objective and subjective image quality of deep learning reconstruction for low-dose abdominal CT imaging in comparison with model-based iterative reconstruction and filtered back projection | Br J Radiol. (2021) | Read article |
| Narita et al. | Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography | Abdom Radiol (NY) 2020 | Read article |
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