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We consider the problem of time-series prediction with missing observations. We consider the autoregressive model (AR model) and cast the problem as a regression problem. On the basic of sampling methods and the online gradient descent (OGD), we propose efficient any-time methods to solve this problem. We show that our algorithm can learn the underlying model efficiently, meanwhile, is robust to the...
Detection of calcified plaques in coronary arteries is helpful in cardiovascular disease risk assessment. This is often performed by radiologists on computed tomography (CT) images. We work towards an automatic solution for calcium detection in CT images. Most of previous work in this area combines CT and CTA for this purpose to facilitate the localization of the coronary arteries. Given the cost...
Quantitative analysis of cardiac Magnetic Resonance (CMR) images requires accurate segmentation of myocardium. Although recent multi-atlas segmentation approaches have done a good job improving segmentation accuracy, they also increase the computational burden, which degrades their clinical utility. In this paper, we proposed a novel multi-atlas segmentation framework using an augmented atlas technique...
Corrective learning is a technique that applies classification methods for automatically detecting and correcting systematic segmentation errors produced by existing segmentation methods with respect to some gold standard (manual) segmentation. To allow corrective learning more effectively correct errors that require non-local contextual information to capture, we extend the corrective learning technique...
Bone fractures are among the most common traumas in musculoskeletal injuries. They are also frequently missed during the radiological examination. Thus, there is a need for assistive technologies for radiologists in this field. Previous automatic bone fracture detection work has focused on detection of specific fracture types in a single anatomical region. In this paper, we present a generalized bone...
Anatomical structure labeling in echocardiogram images will assist cardiac disease diagnosis by providing a framework for doing geometrical statistics. General labeling algorithms often focus on stationary body structures and do not perform well in echocardiography due to cardiac motion, low signal to noise ratio, and structural deformation caused by diseases. In this paper, we propose a new method...
Multi-atlas label fusion has been widely applied in medical image analysis. To reduce the bias in label fusion, we proposed a joint label fusion technique to reduce correlated errors produced by different atlases via considering the pair-wise dependencies between them. Using image similarities from image patches to estimate the pairwise dependencies, we showed promising performance. To address the...
We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new classifier ensemble algorithm to correct segmentation errors produced by a multi-atlas based segmentation method. Our classifier ensemble algorithm searches for the maximum likelihood solution via gradient ascent optimization. Compared to the additive regression based algorithm, LogitBoost, our algorithm avoids...
We develop a semi-automatic technique for segmentation of hippocampal subfields in T2-weighted in vivo brain MRI. The technique takes the binary segmentation of the whole hippocampus as input, and automatically labels the subfields inside the hippocampus segmentation. Shape priors for the hippocampal subfields are generated from shape-based normalization of whole hippocampi via the continuous medial...
By investigating and researching for the present state of computer basic courses teaching in domestic and foreign technology universities, some problems on computer basic courses teaching are summarized and analyzed in this paper. Update education ideas and teaching methods, reference to the advanced teaching and practice experience of computer basic courses from foreign countries and years of teaching...
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