varunr14/newsummary huggingface dataset:
Recently various non invasive tools such as the magnetic resonance image ultrasound imaging computed tomography and the computational fluid dynamics have been widely utilized to enhance our current understanding of the physiological parameters that affect the initiation and the progression of the cardiovascular diseases associated with heart failure . In particular the hemodynamics of left ventricle has attracted the attention of the researchers due to its significant role in the heart functionality . In this study CFD owing its capability of predicting detailed flow field was adopted to model the blood flow in images based patient specific LV over cardiac cycle . In most published studies the blood is modeled as Newtonian that is not entirely accurate as the blood viscosity varies with the shear rate in non linear manner . In this paper we studied the effect of Newtonian assumption on the degree of accuracy of intraventricular hemodynamics . In doing so various non Newtonian models and Newtonian model are used in the analysis of the intraventricular flow and the viscosity of the blood . Initially we used the cardiac MRI images to reconstruct the time resolved geometry of the patient specific LV . After the unstructured mesh generation the simulations were conducted in the CFD commercial solver FLUENT to analyze the intraventricular hemodynamic parameters . The findings indicate that the Newtonian assumption can not adequately simulate the flow dynamic within the LV over the cardiac cycle which can be attributed to the pulsatile and recirculation nature of the flow and the low blood shear rate . 


Summary: Aim investigate the effect of different rheological models of blood within the LV. Model the MRI images are used to reconstruct the time resolved geometry of LV. Finding the non Newtonian assumption is significant on the LV flow dynamics.
Background and objective In this paper we have tested the suitability of using different artificial intelligence based algorithms for decision support when classifying the risk of congenital heart surgery . In this sense classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair and other elements that influence the final result . This preventive estimation may help to avoid future complications or even death . Methods We have evaluated four machine learning algorithms to achieve our objective multilayer perceptron self organizing map radial basis function networks and decision trees . The architectures implemented have the aim of classifying among three types of surgical risk low complexity medium complexity and high complexity . Results Accuracy outcomes achieved range between 80 and 99 being the multilayer perceptron method the one that offered a higher hit ratio . Conclusions According to the results it is feasible to develop a clinical decision support system using the evaluated algorithms . Such system would help cardiology specialists paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery . 


Summary: We propose an alternative system for classifying the risk in paediatric congenital heart surgery. Four methods are tested a perceptron multilayer self organising maps a radial basis function neural network and decision trees. We obtain an accuracy of 99.87 using pre and post surgical data and 83 using just pre surgical data .
Background and objective The adoption of computerized physician order entry is an important cornerstone of using health information technology in health care . The transition from paper to computer forms presents a change in physicians practices . The main objective of this study was to investigate the impact of implementing a computer based order entry system without clinical decision support on the number of radiographs ordered for patients admitted in the emergency department . Methods This single center pre post intervention study was conducted in January 2013 and January 2014 at the emergency department at N mes University Hospital . All patients admitted in the emergency department who had undergone medical imaging were included in the study . Results Emergency department admissions have increased since the implementation of CPOE . In the period before CPOE implementation 2345 patients had undergone medical imaging in the period after CPOE implementation 2306 patients had undergone medical imaging . In the period before CPOE 2916 medical imaging procedures were ordered in the period after CPOE 2876 medical imaging procedures were ordered . In the period before CPOE 1885 radiographs were ordered in the period after CPOE 1776 radiographs were ordered . The time between emergency department admission and medical imaging did not vary between the two periods . Conclusions Our results show a decrease in the number of radiograph requests after a CPOE system without clinical decision support was implemented in our emergency department . 


Summary: The main objective of this study was to investigate the impact of implementing a computer based order entry system without clinical decision support on the number of radiographs ordered for patients seen in the emergency department. Our results show a decrease in the number of radiographs ordered after computer based order entry system implementation despite an increase in the number of emergency department admissions. Our study also shows that the time interval between emergency department admission and medical imaging was not affected by this new workflow.
Object tracking quality usually depends on video scene conditions . In order to overcome this limitation this article presents a new control approach to adapt the object tracking process to the scene condition variations . More precisely this approach learns how to tune the tracker parameters to cope with the tracking context variations . The tracking context or context of a video sequence is defined as a set of six features density of mobile objects their occlusion level their contrast with regard to the surrounding background their contrast variance their 2D area and their 2D area variance . In an offline phase training video sequences are classified by clustering their contextual features . Each context cluster is then associated to satisfactory tracking parameters . In the online control phase once a context change is detected the tracking parameters are tuned using the learned values . The approach has been experimented with three different tracking algorithms and on long complex video datasets . This article brings two significant contributions a classification method of video sequences to learn offline tracking parameters and a new method to tune online tracking parameters using tracking context . 


Summary: We present a new control approach to adapt trackers to scene condition variations. Tracking context is defined as six features describing scene condition. Best tracker parameters are learned offline for tracking contexts. Trackers are then controlled by tuning online their parameters. Experimental results are compared with several recent state of the art trackers.
Glomerulus diameter and Bowman s space width in renal microscopic images indicate various diseases . Therefore the detection of the renal corpuscle and related objects is a key step in histopathological evaluation of renal microscopic images . However the task of automatic glomeruli detection is challenging due to their wide intensity variation besides the inconsistency in terms of shape and size of the glomeruli in the renal corpuscle . Here a novel solution is proposed which includes the Particles Analyzer technique based on median filter for morphological image processing to detect the renal corpuscle objects . Afterwards the glomerulus diameter and Bowman s space width are measured . The solution was tested with a dataset of 21 rats renal corpuscle images acquired using light microscope . The experimental results proved that the proposed solution can detect the renal corpuscle and its objects efficiently . As well as the proposed solution has the ability to manage any input images assuring its robustness to the deformations of the glomeruli even with the glomerular hypertrophy cases . Also the results reported significant difference between the control and affected of fructose groups in terms of glomerulus diameter . 


Summary: Glomerulus diameter and Bowman s capsule thickness in renal microscopic images indicate various diseases. Detection of the renal corpuscle and related objects detection is a key step in histopathological evaluation of renal microscopic images. This work proposed the Analysis Particles algorithm based on median filter for morphological image processing to detect the renal corpuscle objects. Afterwards the glomerulus diameter and Bowman s capsule thickness are measured. The proposed system was tested with a dataset of 21 rats renal corpuscle images acquired using light microscope. The experimental results proven that the proposed solution can detect the renal corpuscle and its objects efficiently with robustness to the deformations of the glomeruli even in the case of glomerular hypertrophy that leads to split the bowman space. The results also reported significant difference between the controlled and affected groups in terms of glomerulus diameter 97.40 19.02 m and 177.03 54.48 m respectively .
Background and objective Optimal experimental design approaches are seldom used in preclinical drug discovery . The objective is to develop an optimal design software tool specifically designed for preclinical applications in order to increase the efficiency of drug discovery in vivo studies . Methods Several realistic experimental design case studies were collected and many preclinical experimental teams were consulted to determine the design goal of the software tool . The tool obtains an optimized experimental design by solving a constrained optimization problem where each experimental design is evaluated using some function of the Fisher Information Matrix . The software was implemented in C using the Qt framework to assure a responsive user software interaction through a rich graphical user interface and at the same time achieving the desired computational speed . In addition a discrete global optimization algorithm was developed and implemented . Results The software design goals were simplicity speed and intuition . Based on these design goals we have developed the publicly available software PopED lite . Optimization computation was on average over 14 test problems 30 times faster in PopED lite compared to an already existing optimal design software tool . PopED lite is now used in real drug discovery projects and a few of these case studies are presented in this paper . Conclusions PopED lite is designed to be simple fast and intuitive . Simple to give many users access to basic optimal design calculations . Fast to fit a short design execution cycle and allow interactive experimental design . Intuitive so that the input to and output from the software tool can easily be understood by users without knowledge of the theory of optimal design . In this way PopED lite is highly useful in practice and complements existing tools . 


Summary: We have created a software PopED lite in order to increase the use of optimal design in preclinical drug discovery. PopED lite is designed to be simple fast and intuitive so that it is highly useful in practice and complements existing tools. Key functionality of PopED lite is demonstrated by three case studies from real drug discovery projects.
Wildfire smoke detection is particularly important for early warning systems because smoke usually rises before flames arise . Therefore this paper presents an automatic wildfire smoke detection method using computer vision and pattern recognition techniques . First candidate blocks are identified using key frame differences and nonparametric smoke color models to detect smoke colored moving objects . Subsequently three dimensional spatiotemporal volumes are built by combining the candidate blocks in the current key frame with the corresponding blocks in previous frames . A histogram of oriented gradient is extracted and a histogram of oriented optical flow is extracted as a temporal feature based on the fact that the direction of smoke diffusion is upward owing to thermal convection . From spatiotemporal features of training data a visual codebook and a bag of features histogram are generated using our proposed weighting scheme . For smoke verification a random forest classifier is built during the training phase using the BoF histogram . The random forest with the BoF histogram can increase the detection accuracy performance when compared with related methods and allow smoke detection to be carried out in near real time . 


Summary: We select key frames from a video and detect candidate blocks only in key frames. We prepare 3D spatiotemporal volumes by combining the candidate blocks. We introduce a new weighting scheme for generating a more reasonable BoF. The random forest classifier is built during the training phase by using the BoF.
This paper examines the issue of face speaker and bi modal authentication in mobile environments when there is significant condition mismatch . We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone . To perform these experiments we develop three novel authentication protocols for the large publicly available MOBIO database . We evaluate state of the art face speaker and bi modal authentication techniques and show that inter session variability modelling using Gaussian mixture models provides a consistently robust system for face speaker and bi modal authentication . It is also shown that multi algorithm fusion provides a consistent performance improvement for face speaker and bi modal authentication . Using this bi modal multi algorithm system we derive a state of the art authentication system that obtains a half total error rate of 6.3 and 1.9 for Female and Male trials respectively . 


Summary: We examine bi modal face speaker authentication in challenging mobile environment. We release new protocols and data with significant mismatch conditions MOBIO . We study bi modal and multi algorithm fusion using generative modelling techniques. Multi algorithm and multi modal fusion provides a consistent performance improvement. The proposed bi modal system significantly outperforms the state of the art.
Background and objectives Automatic electrocardiogram heartbeat classification is substantial for diagnosing heart failure . The aim of this paper is to evaluate the effect of machine learning methods in creating the model which classifies normal and congestive heart failure on the long term ECG time series . Methods The study was performed in two phases feature extraction and classification phase . In feature extraction phase autoregressive Burg method is applied for extracting features . In classification phase five different classifiers are examined namely C4.5 decision tree k nearest neighbor support vector machine artificial neural networks and random forest classifier . The ECG signals were acquired from BIDMC Congestive Heart Failure and PTB Diagnostic ECG databases and classified by applying various experiments . Results The experimental results are evaluated in several statistical measures and showed that the random forest method gives 100 classification accuracy . Conclusions Impressive performance of random forest method proves that it plays significant role in detecting congestive heart failure and can be valuable in expressing knowledge useful in medicine . 


Summary: Heartbeat classification is substantial for diagnosing heart failure. Machine learning methods classify normal and congestive heart failure CHF . The random forest method gives 100 classification accuracy in detecting CHF.
Text contained in scene images provides the semantic context of the images. For that reason robust extraction of text regions is essential for successful scene text understanding. However separating text pixels from scene images still remains as a challenging issue because of uncontrolled lighting conditions and complex backgrounds. In this paper we propose a two stage conditional random field TCRF approach to robustly extract text regions from the scene images. The proposed approach models the spatial and hierarchical structures of the scene text and it finds text regions based on the scene text model. In the first stage the system generates multiple character proposals for the given image by using multiple image segmentations and a local CRF model. In the second stage the system selectively integrates the generated character proposals to determine proper character regions by using a holistic CRF model. Through the TCRF approach we cast the scene text separation problem as a probabilistic labeling problem which yields the optimal label configuration of pixels that maximizes the conditional probability of the given image. Experimental results indicate that our framework exhibits good performance in the case of the public databases. 


Summary: Proposed system separates text regions from images under unconstrained environment. Generalized clustering utilizes properties of scene text to detect text boundaries. Multiple image segmentations provide various interpretations on text regions. Two step CRF approach models properties and relationship of text in graph structure. Character proposals are generated and integrated to find proper character regions.
Vibroarthographic signals emitted from the knee joint disorder provides an early diagnostic tool . The nonstationary and nonlinear nature of VAG signal makes an important aspect for feature extraction . In this work we investigate VAG signals by proposing a wavelet based decomposition . The VAG signals are decomposed into sub band signals of different frequencies . Nonlinear features such as recurrence quantification analysis approximate entropy and sample entropy are extracted as features of VAG signal . A total of twenty four features form a vector to characterize a VAG signal . Two feature selection techniques apriori algorithm and genetic algorithm selects six and four features as the most significant features . Least square support vector machines and random forest are proposed as classifiers to evaluate the performance of FS techniques . Results indicate that the classification accuracy was more prominent with features selected from FS algorithms . Results convey that LS SVM using the apriori algorithm gives the highest accuracy of 94.31 with false discovery rate of 0.0892 . The proposed work also provided better classification accuracy than those reported in the previous studies which gave an accuracy of 88 . This work can enhance the performance of existing technology for accurately distinguishing normal and abnormal VAG signals . And the proposed methodology could provide an effective non invasive diagnostic tool for knee joint disorders . 


Summary: We proposed RQA ApEn SampEn and wavelet based energy as feature extraction techniques. We have proposed feature selection algorithm to extract the most significant and relevant features. We have used LS SVM and random forest as classifiers. Performance among feature selection algorithms are compared.
We present a new method for multi agent activity analysis and recognition that uses low level motion features and exploits the inherent structure and recurrence of motion present in multi agent activity scenarios. Our representation is inspired by the need to circumvent the difficult problem of tracking in multi agent scenarios and the observation that for many visual multi agent recognition tasks the spatiotemporal description of events irrespective of agent identity is sufficient for activity classification. We begin by learning generative models describing motion induced by individual actors or groups which are considered to be agents. These models are Gaussian mixture distributions learned by linking clusters of optical flow to obtain contiguous regions of locally coherent motion. These possibly overlapping regions or segments known as motion patterns are then used to analyze a scene by estimating their spatial and temporal relationships. The geometric transformations between two patterns are obtained by iteratively warping one pattern onto another whereas the temporal relationships are obtained from their relative times of occurrence within videos. These motion segments and their spatio temporal relationships are represented as a graph where the nodes are the statistical distributions and the edges have geometric transformations between motion patterns transformed to Lie space as their attributes. Two activity instances are then compared by estimating the cost of attributed inexact graph matching. We demonstrate the application of our framework in the analysis of American football plays a typical multi agent activity. The performance analysis of our method shows that it is feasible and easily generalizable. 


Summary: Modeling recognition of multi agent activities American football plays . Activities modeled as graphs inexact graph matching used for comparison. Single agent activity represented as motion patterns modeled as graph nodes. Spatio temporal relationships between single agent behaviors modeled as graph edges. We present our own dataset of football plays called UCF Football .
Objective Cancer is the primary disease responsible for death and disability worldwide . Currently prevention and early detection represents the best hope for cure . Knowing the expected diseases that occur with a particular cancer in advance could lead to physicians being able to better tailor their treatment for cancer . The aim of this study was to build an animated visualization tool called as Cancer Associations Map Animation to chart the association of cancers with other disease over time . Methods The study population was collected from the Taiwan National Health Insurance Database during the period January 2000 to December 2002 782 million outpatient visits were used to compute the associations of nine major cancers with other diseases . A motion chart was used to quantify and visualize the associations between diseases and cancers . Results The CAMA motion chart that was built successfully facilitated the observation of cancer disease associations across ages and genders . The CAMA system can be accessed online at http 203.71.86.98 web runq16.html . Conclusion The CAMA animation system is an animated medical data visualization tool which provides a dynamic time lapse animated view of cancer disease associations across different age groups and gender . Derived from a large nationwide healthcare dataset this exploratory data analysis tool can detect cancer comorbidities earlier than is possible by manual inspection . Taking into account the trajectory of cancer specific comorbidity development may facilitate clinicians and healthcare researchers to more efficiently explore early stage hypotheses develop new cancer treatment approaches and identify potential effect modifiers or new risk factors associated with specific cancers . Motion chart parameters Mapping variables in this study Time Presents age of patients X axis Presents the scale of association s strength Y axis Presents the scale of count number of relative disease Size of circle Presents the number of co occurrence of both diseases A and B Color Presents the category of disease 


Summary: A novel approach for visualization of temporal patterns focused on the association of cancers with other diseases. A dynamic animation of cancer disease association across different age groups and gender. Identifying comorbidity relationships and providing more information for medical researchers.