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|Title:||Unsupervised medical image classification by combining case-based classifiers|
traumatic brain injury
|Citation:||Dinh, T.A., Silander, T., Su, B., Gong, T., Pang, B.C., Lim, C.C.T., Lee, C.K., Tan, C.L., Leong, T.-Y. (2013). Unsupervised medical image classification by combining case-based classifiers. Studies in Health Technology and Informatics 192 (1-2) : 739-743. ScholarBank@NUS Repository. https://doi.org/10.3233/978-1-61499-289-9-739|
|Abstract:||We introduce an automated pathology classification system for medical volumetric brain image slices. Existing work often relies on handcrafted features extracted from automatic image segmentation. This is not only a challenging and time-consuming process, but it may also limit the adaptability and robustness of the system. We propose a novel approach to combine sparse Gabor-feature based classifiers in an ensemble classification framework. The unsupervised nature of this non-parametric technique can significantly reduce the time and effort for system calibration. In particular, classification of medical images in this framework does not rely on segmentation, nor semantic-based or annotation-based feature selection. Our experiments show very promising results in classifying computer tomography image slices into pathological classes for traumatic brain injury patients. © 2013 IMIA and IOS Press.|
|Source Title:||Studies in Health Technology and Informatics|
|Appears in Collections:||Staff Publications|
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