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Carotid artery lesions could be the pathology of subclinical atherosclerosis and hence lead to the onset of stroke. Early detection of abnormal carotid artery might help to better identify individuals susceptible to stroke. Considering the carotid artery ultrasonography is time-consuming and costly, the object of this paper is to establish a model to detect the status of carotid artery for preliminary screening of stroke, according to the simple physiological examination and survey information. However, most of the previous studies were based on the linear regression or the traditional machine learning methods, those suffer from two limitations. One is the limited labeled samples, and the other one is the missing data. To address these issues, we firstly propose a semi-supervised approach based on a modified variational autoencoder (VAE) to identify the abnormal carotid arteries. In this paper, a mixture of mean and K th nearest neighbours (MKNN) and a modified VAE were used for missing data imputation. The experimental results demonstrate that the proposed method can not only handle the missing values, but also outperform four widely used supervised approaches. Therefore, we can conclude that this semi-supervised model is a promising way to identify the abnormal carotid arteries.

Non-negative matrix factorization (NMF) has been one of the most popular methods for feature learning in the field of machine learning and computer vision. Most existing works directly apply NMF on high-dimensional image datasets for computing the effective representation of the raw images. However, in fact, the common essential information of a given class of images is hidden in their low rank parts. For obtaining an effective low-rank data representation, we in this paper propose a non-negative low-rank matrix factorization (NLMF) method for image clustering. For the purpose of improving its robustness for the data in a manifold structure, we further propose a graph regularized NLMF by incorporating the manifold structure information into our proposed objective function. Finally, we develop an efficient alternating iterative algorithm to learn the low-dimensional representation of low-rank parts of images for clustering. Alternatively, we also incorporate robust principal component analysis into our proposed scheme. Experimental results on four image datasets reveal that our proposed methods outperform four representative methods.

To our knowledge this paper is the first to use recursive partitioning analysis (RPA) for brainstem metastasis (BSM) patient outcomes, after CyberKnife radiosurgery (CKRS; Accuray, Sunnyvale, CA, USA); nine similar previous publications used mainly Gamma Knife radiosurgery (Elekta AB, Stockholm, Sweden). Retrospective chart reviews from 2006-2013 of 949 CKRS-treated brain metastasis patients showed 54 BSM patients (5.7%): 35 RPA Class II (65%) and 19 Class III (35%). There were 30 women (56%) and 24 men (44%). The median age was 59 years (range 36-80) and median follow-up was 5 months (range 1-52). Twenty-three patients (43%) had lung carcinoma BSM and 12 (22%) had breast cancer BSM. Fifty-four RPA Class II and III BSM patients had a median overall survival (OS) of 5 months, and for each Class 8 and 2 months, respectively. Of 36 RPA Class II and III patients with available symptoms (n=31) and findings (n=33), improvement/stability occurred in the majority for symptoms (86%) and findings (92%). Of 35 cases, 28 (80%) achieved BSM local control (LC); 13/14 with breast histology (93%) and 10/13 with lung histology (77%). All six RPA Class II and III patients with controlled extracranial systemic disease (ESD) experienced LC. Median tumor volume was 0.14 cm(3); of 34 RPA Class II and III cases, 26 LC patients had a 0,13 cm(3) median tumor volume while it was 0.27 cm(3) in the eight local failures. Of 35 cases, single session equivalent dosages less than the median (n=13), at the 17.9 Gy median (n=5) and greater than the median (n=17) had BSM LC in 10 (77%), four (80%) and 14 cases (82%), respectively. Univariate analysis showed Karnofsky Performance Score, RPA Class and ESD-control predicted OS. CKRS is useful for RPA Class II and III BSM patients with effective clinical and local BSM control.