Brain tumor detection using fusion of hand crafted and deep learning features

The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor cells may lead to death. Therefore, early brain tumor diagnosis is a more momentous task that can save patient from adverse effects. In the proposed work, the Grab cut method is applied for accurate segmentation of actual lesion symptoms while Transfer learning model visual geometry group (VGG-19) is fine-tuned to acquire the features which are then concatenated with hand crafted (shape and texture) features through serial based method. These features are optimized through entropy for accurate and fast classification and fused vector is supplied to classifiers. The presented model is tested on top medical image computing and computer-assisted intervention (MICCAI) challenge databases including multimodal brain tumor segmentation (BRATS) 2015, 2016, and 2017 respectively. The testing results with dice similarity coefficient (DSC) achieve 0.99 on BRATS 2015, 1.00 on BRATS 2015 and 0.99 on BRATS 2017 respectively.     

Pheochromocytoma as a catecholamine producing tumor: implications for clinical practice

Pheochromocytomas are catecholamine-producing tumors presenting with various clinical symptoms, but mostly with headache, sweating, palpitations and hypertension. If not properly diagnosed, secretion of catecholamines may lead to fatal cardiovascular consequences. Biochemical testing for pheochromocytoma should be performed not only in symptomatic subjects or in subjects with adrenal incidentaloma but also in subjects with a genetic predisposition for pheochromocytoma (multiple endocrine neoplasia type 2, Von Hippel-Lindau (VHL) syndrome, neurofibromatosis type 1 (NF 1)and mutations of succinate dehydrogenase (SDH) genes). Once a pheochromocytoma is proven, computed tomography (CT), magnetic resonance imaging (MRI) and functional imaging with [(123)I]-MIBG may be used for tumor localization. Adequate medical pre-treatment is essential for successful operation which is performed in most cases by laparoscopy. After tumor removal, further follow-up is necessary due to possible recurrence. Although prognosis after tumor resection is excellent, a significant proportion of pheochromocytomas recur, some as metastases. Thus, appropriate follow-up is mandatory.     

4例肾上腺区局限性Castleman病临床诊治分析

探讨肾上腺区局限性Castleman病的临床特点及诊治方法。回顾性分析4例经术后病理证实的肾上腺区局限性 Castle-man病患者的临床资料,并结合文献复习讨论Castleman病的临床特点及诊治方法。结果4例肾上腺区局限性Castleman病均顺利手术切除,术后随访1个月～22个月,未见肿瘤复发。Castleman病临床上少见,表现为不明原因的淋巴结肿大,临床表现无特异性,最终确诊依靠病理诊断。病变可发生在淋巴组织的任何部位,肾上腺区罕见,手术切除为首选治疗。     

多发性硬化诊疗指南评析

我国无统一的多发性硬化（MS）诊断标准,MS的诊断依据Poser标准和McDonald标准。McDonald标准是建立在Poser标准基础上,但更注意利用包括MRI在内的相关实验室检查来证明多发性硬化在时间和空间上的多发性,有利于MS的早期诊断及治疗。对于没有MRI仪器的地方或不能承担MRI检查费用者,可用Poser标准进行诊断,但要注意行视觉诱发电位检查及脑脊液寡克隆IgG区带测定。     

Paroxysmal compulsion to handle keys in a computer operator due to meningioma in the left supplementary motor area

We describe the case of a computer operator who experienced paroxysmal attacks several times in which she felt a compulsion to handle keys with her right hand or actually her right hand moved involuntarily in a key-handling rhythm. Cranial CT and MRI revealed a mass lesion in the left medial aspect of the frontal lobe (supplementary motor area). After the removal of this tumor (meningioma), there were no more paroxysmal attacks. We suggest that voluntary movements controlled by the supplementary motor area were deranged by seizures provoked by the tumor. This case is attractive in relation to obsessive-compulsive disorder.     

Building a cognitive map by assembling multiple path integration systems

Path integration and cognitive mapping are two of the most important mechanisms for navigation. Path integration is a primitive navigation system which computes a homing vector based on an animal’s self-motion estimation, while cognitive map is an advanced spatial representation containing richer spatial information about the environment that is persistent and can be used to guide flexible navigation to multiple locations. Most theories of navigation conceptualize them as two distinctive, independent mechanisms, although the path integration system may provide useful information for the integration of cognitive maps. This paper demonstrates a fundamentally different scenario, where a cognitive map is constructed in three simple steps by assembling multiple path integrators and extending their basic features. The fact that a collection of path integration systems can be turned into a cognitive map suggests the possibility that cognitive maps may have evolved directly from the path integration system.     

Automatic detection of ischemic stroke using higher order spectra features in brain MRI images

The gravity of ischemic stroke is the key factor in deciding upon the optimum therapeutic intervention. Ischemic stroke can be divided into three main groups: lacunar syndrome (LACS), partial anterior circulation syndrome (PACS), and total anterior circulation stroke (TACS), where the corresponding severity is mild, medium, and high, respectively. Herein, a unique method for the automatic detection of ischemic stroke severity is presented. The proposed system is based upon the extraction of higher order bispectrum entropy and its phase features from brain MRI (Magnetic Resonance Imaging) images. For classification, which is used to establish stroke severity, a support vector machine was incorporated into the design. The developed technique effectively detected the stroke lesion, and achieved a sensitivity, specificity, accuracy, and positive predictive value equal to 96.4%, 100%, 97.6% and 100%, respectively. The results were obtained without the need for manual intervention. This design is advantageous over state-of-the-art automated stroke severity detection systems, which require the reading neuroradiologist to manually determine the region of interest. Hence, the method is efficacious for delivering decision support in the diagnosis of ischemic stroke severity, thereby aiding the neuroradiologist in routine screening procedures.     

Decomposing the brain: components and modes, networks and nodes

Smith and colleagues recently presented a temporal independent component analysis (tICA) decomposition of resting-state functional MRI data. Compared to the widely used spatial ICA (sICA), tICA better allows for a brain region to engage in multiple, independent interactions with other regions and will potentially offer new insights into brain function.     

Building a controllable expressive speech synthesis system with multiple emotion strengths

Emotion is considered to be an essential element in the performance of human-computer interactions. In expressive synthesis speech, it is important to generate emotional speech that reflects subtle and complex emotional states. However, there has been limited research on how to effectively synthesize emotional speech using different levels of emotion strength with intuitive control, which is difficult to be modeled effectively. In this paper, we explore an expressive speech synthesis model that can be used to produce speech with multiple emotion strengths. Unlike previous studies that encoded emotions into discrete codes, we propose an embedding vector to continuously control the emotion strength, which is a data-driven method to synthesize speech with a fine control over the emotions. Compared with the models using the retraining technique or a one-hot vector, our proposed model using an embedding vector can explicitly learn the high-level emotion strength from the low-level acoustic features. As a result, we can control the emotion strength of synthetic speech in a relatively predictable and globally consistent way. The objective and subjective evaluation tests show that our proposed model achieves state-of-the-art performance in terms of model flexibility and controllability.     

多重样例变异性与先前知识对样例学习效果的影响述评

样例学习研究表明多重样例的学习效果好于单个样例。然而研究者关于如何有效设计多重样例得出了不同的发现。通过综述已有关于多重样例学习的实证研究,总结出两个尚未得到解决的问题：（1）多重样例之间的相似或相异程度应该如何;（2）学生的先前知识在多重样例学习中的作用如何。针对已有研究的局限性,本文指出,具有不同先前知识的学生学习时感受到不同的关键属性,多重样例应该基于学生学习时的关键属性设计特定的变易范式以帮助学生学习。     

Perception of Happening: How the Brain Deals with the No-History Problem

In physics, the temporal dimension has units of infinitesimally brief duration. Given this, how is it possible to perceive things, such as motion, music, and vibrotactile stimulation, that involve extension across many units of time? To address this problem, it is proposed that there is what is termed an “information construct of happening” (ICOH), a simultaneous representation of recent, temporally differentiated perceptual information on the millisecond time scale. The main features of the ICOH are (i) time marking, semantic labeling of all information in the ICOH with ordinal temporal information and distance from what is informationally identified as the present moment, (ii) vector informational features that specify kind, direction, and rate of change for every feature in a percept, and (iii) connectives, information relating vector informational features at adjacent temporal locations in the ICOH. The ICOH integrates products of perceptual processing with recent historical information in sensory memory on the subsecond time scale. Perceptual information about happening in informational sensory memory is encoded in semantic form that preserves connected semantic trails of vector and timing information. The basic properties of the ICOH must be supported by a general and widespread timing mechanism that generates ordinal and interval timing information and it is suggested that state-dependent networks may suffice for that purpose. Happening, therefore, is perceived at a moment and is constituted by an information structure of connected recent historical information.     

The efficiency of feature-based subitization and counting

The enumeration of small numbers of objects (approximately 4) proceeds rapidly, accurately, and with little effort via a process termed subitization. Four experiments examined whether it was possible to subitize the number of features rather than objects present in a display. Overall, the findings showed that when features are presented randomly and are uncorrelated with object numerosity, efficient enumeration is not possible. This suggests that the visual system does not have parallel access to multiple feature maps and that subitization processes operate exclusively on representations coding the locations of objects. The data are discussed with respect to theories of visual enumeration and search.     

心率变异性与人格的神经质之间关系研究

本研究探讨心率变异性（HRV）特征在高水平应激条件下对神经质水平的区分力。研究选取200名被试以大五人格量表测量情绪稳定性,实施了应激诱发实验,利用光体积扫描传感器采集被试者HRV指标;利用LASSO回归分析筛选参数构建对神经质的预测模型。结果显示：模型的预测数据与神经质得分高低显著正相关;不同阶段内预测数据与神经质得分显著正相关;两两比较中实验条件阶段与其他阶段预测值差异显著。研究表明,在高应激条件下,HRV是人格神经质较好的预测指标。     

Developing and Improving After-School Programs to Enhance Youth’s Personal Growth and Adjustment: A Special Issue of AJCP

Participating in after-school programs (ASPs) has become a common experience for children. This special issue provides a perspective on the current status of research on ASPs. This introductory article overviews the historical and current context of ASPs and then describes a developmental ecological model to guide research in this area. The model offers a framework from which to organize and synthesize the research presented in this issue. Key principles include a holistic view of development that recognizes interrelations between multiple domains of youth adjustment, attention to multiple, relevant factors within and outside of youth that affect development, examining the dynamic interplay between persons, program features, and other contexts over time, and understanding the active role of youth in affecting their own development. These principles are examined in relation to five main areas: youth characteristics, social ecologies, program features, participation, and short- and long-term outcomes. Recommendations for future research are discussed.     

Multiple independent identification decisions: a method of calibrating eyewitness identifications

Two experiments (N = 147 and N = 90) explored the use of multiple independent lineups to identify a target seen live. In Experiment 1, simultaneous face, body, and sequential voice lineups were used. In Experiment 2, sequential face, body, voice, and clothing lineups were used. Both studies demonstrated that multiple identifications (by the same witness) from independent lineups of different features are highly diagnostic of suspect guilt (G. L. Wells & R. C. L. Lindsay, 1980). The number of suspect and foil selections from multiple independent lineups provides a powerful method of calibrating the accuracy of eyewitness identification. Implications for use of current methods are discussed.     

The return of object-based attention: selection of multiple-region objects

Objects can control the focus of attention, allowing features on the same object to be selected more easily than features on different objects. In the present experiments, we investigated the perceptual processes that contribute to such object-based attentional effects. Previous research has demonstrated that object-based effects occur for single-region objects but not for multiple-region objects under some conditions (Experiment 1, Watson & Kramer, 1999). Such results are surprising, because most objects in natural scenes are composed of multiple regions. Previous findings could therefore limit the usefulness of an object-based selection mechanism. We explored the generality of these single-region selection results by manipulating the extent to which different (i.e., multiple) regions of a single object perceptually grouped together. Object-based attentional effects were attenuated when multiple regions did not group into a single perceptual object (Experiment 1). However, when multiple regions grouped together based on (1) edge continuation (Experiments 2 and 3) or (2) part and occlusion cues (Experiment 4), we observed object-based effects. Our results suggest that object-based attention is a robust process that can select multiple-region objects, provided the regions of such objects cohere on the basis of perceptual grouping cues.     

CPCA: A program for principal component analysis with external information on subjects and variables

A program is described for principal component analysis with external information on subjects and variables. This method is calledconstrained principal component analysis (CPCA), in which regression analysis and principal component analysis are combined into a unified framework that allows a full exploration of data structures both within and outside known information on subjects and variables. Many existing methods are special cases of CPCA, and the program can be used for multivariate multiple regression, redundancy analysis, double redundancy analysis, dual scaling with external criteria, vector preference models, and GMANOVA (growth curve models).     

Predication

In Latent Semantic Analysis (LSA) the meaning of a word is represented as a vector in a high-dimensional semantic space. Different meanings of a word or different senses of a word are not distinguished. Instead, word senses are appropriately modified as the word is used in different contexts. In N-VP sentences, the precise meaning of the verb phrase depends on the noun it is combined with. An algorithm is described to adjust the meaning of a predicate as it is applied to different arguments. In forming a sentence meaning, not all features of a predicate are combined with the features of the argument, but only those that are appropriate to the argument. Hence, a different "sense" of a predicate emerges every time it is used in a different context. This predication algorithm is explored in the context of four different semantic problems: metaphor interpretation, causal inferences, similarity judgments, and homonym disambiguation.     

Divergence Behaviour of the Successive Geometric Mean Method of Pairwise Comparison Matrix Generation for a Multiple Stage,Multiple Objective Optimization Problem

This paper focuses on the divergence behaviour of the successive geometric mean (SGM) method used to generate pairwise comparison matrices while solving a multiple stage, multiple objective (MSMO) optimization problem. The SGM method can be used in the matrix generation phase of our three‐phase methodology to obtain pairwise comparison matrix at each stage of an MSMO optimization problem, which can be subsequently used to obtain the weight vector at the corresponding stage. The weight vectors across the stages can be used to convert an MSMO problem into a multiple stage, single objective (MSSO) problem, which can be solved using dynamic programming‐based approaches. To obtain a practical set of non‐dominated solutions (also referred to as Pareto optimal solutions) to the MSMO optimization problem, it is important to use a solution approach that has the potential to allow for a better exploration of the Pareto optimal solution space. To accomplish a more exhaustive exploration of the Pareto optimal solution space, the weight vectors that are used to scalarize the MSMO optimization problem into its corresponding MSSO optimization problem should vary across the stages. Distinct weight vectors across the stages are tied directly with distinct pairwise comparison matrices across the stages. A pairwise comparison matrix generation method is said to diverge if it can generate distinct pairwise comparison matrices across the stages of an MSMO optimization problem. In this paper, we demonstrate the SGM method's divergence behaviour when the three‐phase methodology is used in conjunction with an augmented high‐dimensional, continuous‐state stochastic dynamic programming method to solve a large‐scale MSMO optimization problem. Copyright © 2013 John Wiley & Sons, Ltd.