Abstract
Cerebral small vessel disease (SVD) is a common cause of ageing-associated physical and cognitive impairment. Identifying SVD is important for both clinical and research purposes but is usually dependent on radiologists’ evaluation of brain scans. Computer tomography (CT) is the most widely used brain imaging technique but for SVD it shows a low signal-to-noise ratio, and consequently poor inter-rater reliability. We therefore propose a novel framework based on multiple instance learning (MIL) to distinguish between absent/mild SVD and moderate/severe SVD. Intensity patches are extracted from regions with high probability of containing lesions. These are then used as instances in MIL for the identification of SVD. A large baseline CT dataset, consisting of 590 CT scans, was used for evaluation. We achieved approximately 75% accuracy in classifying two different types of SVD, which is high for this challenging problem. Our results outperform those obtained by either standard machine learning methods or current clinical practice.
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Keywords
- Random Forest
- Recombinant Tissue Plasminogen Activator
- Computer Tomography Image
- Computer Tomography Scan
- Multiple Instance Learning
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Chen, L. et al. (2015). Identification of Cerebral Small Vessel Disease Using Multiple Instance Learning. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9349. Springer, Cham. https://doi.org/10.1007/978-3-319-24553-9_64
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DOI: https://doi.org/10.1007/978-3-319-24553-9_64
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