varunr14/newsummary huggingface dataset:
A toolkit has been developed for calculating the 3 dimensional biological effective dose distributions in multi phase external beam radiotherapy treatments such as those applied in liver stereotactic body radiation therapy and in multi prescription treatments . This toolkit also provides a wide range of statistical results related to dose and BED distributions . MATLAB 2010a version 7.10 was used to create this GUI toolkit . The input data consist of the dose distribution matrices organ contour coordinates and treatment planning parameters from the treatment planning system . The toolkit has the capability of calculating the multi phase BED distributions using different formulas . Following the calculations of the BED distributions the dose and BED distributions can be viewed in different projections . The different elements of this toolkit are presented and the important steps for the execution of its calculations are illustrated . The toolkit is applied on brain head neck and prostate cancer patients who received primary and boost phases in order to demonstrate its capability in calculating BED distributions as well as measuring the inaccuracy and imprecision of the approximate BED distributions . Finally the clinical situations in which the use of the present toolkit would have a significant clinical impact are indicated . 


Summary: A GUI was created to calculate the multi phase BED distributions. The GUI is effective at determining the BED distributions and the dose statistics of the ROIs for multi phase treatment plans. Using this GUI has shown the inaccuracies of using the approximate BED calculation method for multi phase cases. This GUI can be used to optimize multi phase treatment plans.
Background and objectives In computed tomography statistical iterative reconstruction approaches can produce images of higher quality compared to the conventional analytical methods such as filtered backprojection algorithm . Effective noise modeling and possibilities to incorporate priors in the image reconstruction problem are the main advantages that lead to continuous development of SIR methods . Oriented by low dose CT requirements several methods are recently developed to obtain a high quality image reconstruction from down sampled or noisy projection data . In this paper a new prior information obtained from probabilistic atlas is proposed for low dose CT image reconstruction . Methods The proposed approach consists of two main phases . In learning phase a dataset of images obtained from different patients is used to construct a 3D atlas with Laplacian mixture model . The expectation maximization algorithm is used to estimate the mixture parameters . In reconstruction phase prior information obtained from the probabilistic atlas is used to construct the cost function for image reconstruction . Results We investigate the low dose imaging by considering the reduction of X ray beam intensity and by acquiring the projection data through a small number of views or limited view angles . Experimental studies using simulated data and chest screening CT data demonstrate that the probabilistic atlas prior is a practically promising approach for the low dose CT imaging . Conclusions The prior information obtained from probabilistic atlas constructed from earlier scans of different patients is useful in low dose CT imaging . 


Summary: A powerful statistical image reconstruction algorithm for CT is proposed. Data obtained from earlier scans are used to construct a probabilistic atlas with Laplacian mixture model. Prior information obtained from a probabilistic atlas is modeled for the CT image reconstruction. We consider low dose CT imaging setups using proposed method and alternative approaches. The proposed method outperforms other alternative methods in terms of image quality.
Background M2M communications represent one of the main pillars of the new paradigm of the Internet of Things and is making possible new opportunities for the eHealth business . Nevertheless the large number of M2M protocols currently available hinders the election of a suitable solution that satisfies the requirements that can demand eHealth applications . Objectives In the first place to develop a tool that provides a benchmarking analysis in order to objectively select among the most relevant M2M protocols for eHealth solutions . In the second place to validate the tool with a particular use case the respiratory rehabilitation . Methods A software tool called Distributed Computing Framework has been designed and developed to execute the benchmarking tests and facilitate the deployment in environments with a large number of machines with independence of the protocol and performance metrics selected . Results DDS MQTT CoAP JMS AMQP and XMPP protocols were evaluated considering different specific performance metrics including CPU usage memory usage bandwidth consumption latency and jitter . The results obtained allowed to validate a case of use respiratory rehabilitation of chronic obstructive pulmonary disease patients in two scenarios with different types of requirement Home Based and Ambulatory . Conclusions The results of the benchmark comparison can guide eHealth developers in the choice of M2M technologies . In this regard the framework presented is a simple and powerful tool for the deployment of benchmark tests under specific environments and conditions . 


Summary: The software tool presented allows the deployment of different benchmarking tests for M2M protocols. The most relevant M2M protocols were evaluated considering different specific performance metrics. Benchmark results allowed to select the most suitable M2M protocol a clinical case of use respiratory rehabilitation.
Background and objective Transfer function is an important parameter for the analysis and understanding of hemodynamics when arterial stenosis exists in human arterial tree . Aimed to validate the feasibility of using TF to diagnose arterial stenosis the forward problem and inverse problem were simulated and discussed . Methods A calculation method of TF between ascending aorta and any other artery was proposed based on a 55 segment transmission line model of human artery tree . The effects of artery stenosis on TF were studied in two aspects stenosis degree and position . The degree of arterial stenosis was specified to be 10 90 in three representative arteries carotid aorta and iliac artery respectively . In order to validate the feasibility of diagnosis of artery stenosis using TF and support vector machine a database of TF was established to simulate the real conditions of artery stenosis based on the TLM model . And a diagnosis model of artery stenosis was built by using SVM and the database . Results The simulating results showed the modulus and phase of TF were decreasing sharply from frequency 2 to 10Hz with the stenosis degree increasing and displayed their unique and nonlinear characteristics when frequency is higher than 10Hz . The diagnosis results showed the average accuracy was above 76 for the stenosis from 10 to 90 degree and the diagnosis accuracies of moderate and serious stenosis were 87 and 99 respectively . When the stenosis degree increased to 90 the accuracy of stenosis localization reached up to 94 for most of arteries . Conclusions The proposed method of combining TF and SVM is a theoretically feasible method for diagnosis of artery stenosis . 


Summary: A calculation method of transfer function TF was proposed by a TLM model of human artery tree. The effects of artery stenosis on the TF were simulated and discussed by a series of simulation. A novel method of artery stenosis diagnosis was proposed and validated by TF and SVM. The accuracies of the method for moderate and serious stenosis were 87 and 99 respectively. The proposed method is a theoretically feasible method for diagnosis of artery stenosis.
In observational studies without random assignment of the treatment the unadjusted comparison between treatment groups may be misleading due to confounding . One method to adjust for measured confounders is inverse probability of treatment weighting . This method can also be used in the analysis of time to event data with competing risks . Competing risks arise if for some individuals the event of interest is precluded by a different type of event occurring before or if only the earliest of several times to event corresponding to different event types is observed or is of interest . In the presence of competing risks time to event data are often characterized by cumulative incidence functions one for each event type of interest . We describe the use of inverse probability of treatment weighting to create adjusted cumulative incidence functions . This method is equivalent to direct standardization when the weight model is saturated . No assumptions about the form of the cumulative incidence functions are required . The method allows studying associations between treatment and the different types of event under study while focusing on the earliest event only . We present a SAS macro implementing this method and we provide a worked example . 


Summary: We use inverse probability of treatment weighting a propensity score based technique for covariate adjustment of the cumulative incidence functions in competing risk analysis. This method requires no assumption about the form of the cumulative incidence functions and the interpretation of the adjusted cumulative incidence functions is intuitively appealing. We developed a SAS macro to make the method readily usable.
We propose a scheme for comparing local neighborhoods window of image points to estimate optical flow using discrete optimization. The proposed approach is based on using large correlation windows with adaptive support weights. We present three new types of weighting constraints derived from image gradient color statistics and occlusion information. The first type provides gradient structure constraints that favor flow consistency across strong image gradients. The second type imposes perceptual color constraints that reinforce relationship among pixels in a window according to their color statistics. The third type yields occlusion constraints that reject pixels that are seen in one window but not seen in the other. All these constraints contribute to suppress the effect of cluttered background which is unavoidably included in the large correlation windows. Experimental results demonstrate that each of the proposed constraints appreciably elevates the quality of estimations and that they jointly yield results that compare favorably to current techniques especially on object boundaries. 


Summary: A method based on using large correlation windows with adaptive support weights Three new weighting constraints from image gradient color statistics and occlusion Contributes to suppress the effect of cluttered background in the windows Elevates the quality of estimations especially on object boundaries
In this paper the problem of human ear recognition in the Mid wave infrared spectrum is studied in order to illustrate the advantages and limitations of the ear based biometrics that can operate in day and night time environments . The main contributions of this work are two fold First a dual band database is assembled that consists of visible and mid wave IR left and right profile face images . Profile face images were collected using a high definition mid wave IR camera that is capable of acquiring thermal imprints of human skin . Second a fully automated thermal imaging based ear recognition system is proposed that is designed and developed to perform real time human identification . The proposed system tests several feature extraction methods namely intensity based such as independent component analysis principal component analysis and linear discriminant analysis shape based such as scale invariant feature transform as well as texture based such as local binary patterns and local ternary patterns . Experimental results suggest that LTP yields the best performance on manually segmented ears and on ear images that are automatically detected and segmented . By fusing the matching scores obtained by LBP and LTP the identification performance increases by about 5 . Although these results are promising the outcomes of our study suggest that the design and development of automated ear based recognition systems that can operate efficiently in the lower part of the passive IR spectrum are very challenging tasks . 


Summary: Collected thermal profile face database using a middle wave infrared 3 5 microns camera . Developed a fully automated thermal ear recognition system for real time human identification works in day or night . Local Ternary Pattern yields Rank 1 80.68 and 68.18 using manually and automatically segmented ears respectively. Score Level fusion of the Local Ternary Pattern LTP and Local Binary Pattern LBP enhanced the performance by 5 .
On the Semantic Web the types of resources and the semantic relationships between resources are defined in an ontology . By using that information the accuracy of information retrieval can be improved . In this paper we present effective ranking and search techniques considering the semantic relationships in an ontology . Our technique retrieves top k resources which are the most relevant to query keywords through the semantic relationships . To do this we propose a weighting measure for the semantic relationship . Based on this measure we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords . In order to improve the efficiency of the search we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path . In addition we exploit Threshold Algorithm based on an extended inverted index to answer top k results efficiently . The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods . 


Summary: Semantic search using ontology overcomes the limitation of the current keyword based search. The ranking method considering semantic relationships improves the search accuracy. The ranking method is based on the weighting measure of semantic relationships. Pruning based on the weight for the semantic relationship reduces the search space. Top k Answering based on the keyword index improves the search efficiency.
Transfer learning utilizes labeled data available from some related domain for achieving effective knowledge transformation to the target domain . However most state of the art cross domain classification methods treat documents as plain text and ignore the hyperlink relationship existing among the documents . In this paper we propose a novel cross domain document classification approach called Link Bridged Topic model . LBT consists of two key steps . Firstly LBT utilizes an auxiliary link network to discover the direct or indirect co citation relationship among documents by embedding the background knowledge into a graph kernel . The mined co citation relationship is leveraged to bridge the gap across different domains . Secondly LBT simultaneously combines the content information and link structures into a unified latent topic model . The model is based on an assumption that the documents of source and target domains share some common topics from the point of view of both content information and link structure . By mapping both domains data into the latent topic spaces LBT encodes the knowledge about domain commonality and difference as the shared topics with associated differential probabilities . The learned latent topics must be consistent with the source and target data as well as content and link statistics . Then the shared topics act as the bridge to facilitate knowledge transfer from the source to the target domains . Experiments on different types of datasets show that our algorithm significantly improves the generalization performance of cross domain document classification . 


Summary: We propose a Link Bridged Topic model for cross domain document classification. LBT utilizes an auxiliary link network to discover the co citation relationship. LBT combines the content information and link structures into a graphical model. LBT outperforms both multi view learning and single view transfer baselines.
Background modeling is widely used in visual surveillance systems aiming to facilitate analysis of real world video scenes. The goal is to discriminate between pixels from foreground objects and those ones from the background. However real world scenarios tend to have time and spatial non stationary variations being difficult to reveal the foreground and background entities from video data. Here we propose a novel adaptive background modeling termed Object based Selective Updating with Correntropy OSUC to support video based surveillance systems. Our approach that is developed within an adaptive learning framework unveils existing spatio temporal pixel relationships making use of a single Gaussian for the model representation stage. Moreover we introduce a background updating scheme composed of an updating rule that is based on the stochastic gradient algorithm and Correntropy cost function. As a result this scheme can extract the temporal statistical pixel distribution at the same time dealing with non stationary pixel value fluctuations that affect the background model. Here an automatic tuning strategy of the cost function bandwidth parameter is carried out that can handle both Gaussian and non Gaussian noise environments. Besides to include pixel spatial relationships in the background modeling processing we introduce an object based selective learning rate strategy for enhancing the background modeling accuracy. Particularly an object motion analysis stage is presented to detect and track foreground entities based on pixel intensities and motion direction attained via optical flow computation. Testing is provided on well known datasets for discriminating between foreground and background that include stationary and non stationary behaviors. Achieved results show that the OSUC outperforms in most of the considered cases the state of the art approaches with an affordable computational cost. Therefore the proposed approach is suitable for supporting real world video based surveillance systems. 


Summary: We propose a background modeling method to deal with non stationary conditions. The object based updating strategy allows to work with SFO and RFO. Non Gaussian pixel dynamics are modeled using a Correntropy cost function. Analyzing the regions movement direction avoids tracking background objects. Object based updating strategies improve the performance for indoor scenarios.
Video segmentation is a fundamental problem in computer vision and aims to extract meaningful entities from a video. One of the most useful cues in this quest is motion as is described by the trajectories of tracked points. In this paper we present a motion segmentation method attempting to address some of the major issues in the area. Namely we propose an efficient framework where more complex motion models can be seamlessly integrated both maintaining computational tractability and not penalizing non translational motion. Moreover we expose in depth the problem of object leakage due to occlusion and highlight that motion segmentation could be treated as a graph coloring problem. Our algorithm uses an approach based on graph theory and resolves occlusion cases in a robust manner. To endow our method with scalability we follow the previously presented subsequence architecture and test it in a streaming setup. Extensive experiments demonstrate the flexibility and robustness of the method. The segmentation results are competitive compared to the state of the art. 


Summary: Each video is divided in sub sequences. Motion models are extracted for each video sub sequence. The model error distribution and the model ranking for each trajectory are used to correlate different trajectories. Occlusions cause segmentation leakages when sub sequences are merged. To avoid this effect we model segmentation as a graph coloring problem.
Both general and domain specific search engines have adopted query suggestion techniques to help users formulate effective queries . In the specific domain of literature search the initial queries are usually based on a draft paper or abstract rather than short lists of keywords . In this paper we investigate phrasal concept query suggestions for literature search . These suggestions explicitly specify important phrasal concepts related to an initial detailed query . The merits of phrasal concept query suggestions for this domain are their readability and retrieval effectiveness phrasal concepts are natural for academic authors because of their frequent use of terminology and subject specific phrases and academic papers describe their key ideas via these subject specific phrases and thus phrasal concepts can be used effectively to find those papers . We propose a novel phrasal concept query suggestion technique that generates queries by identifying key phrasal concepts from pseudo labeled documents and combines them with related phrases . Our proposed technique is evaluated in terms of both user preference and retrieval effectiveness . We conduct user experiments to verify a preference for our approach in comparison to baseline query suggestion methods and demonstrate the effectiveness of the technique with retrieval experiments . 


Summary: A phrasal concept query suggestion method for literature searches is proposed. Key phrasal concepts are suggested with related concepts. Evaluation was performed using two test collections. User experiments verify preferences to use phrasal concept queries. Phrasal concepts can lead significant improvements over state of the art baselines.
This paper presents an orthonormal dictionary learning method for low rank representation . The orthonormal property encourages the dictionary atoms to be as dissimilar as possible which is beneficial for reducing the ambiguities of representations and computation cost . To make the dictionary more discriminative we enhance the ability of the class specific dictionary to well represent samples from the associated class and suppress the ability of representing samples from other classes and also enforce the representations that have small within class scatter and big between class scatter . The learned orthonormal dictionary is used to obtain low rank representations with fast computation . The performances of face recognition demonstrate the effectiveness and efficiency of the method . 


Summary: A discriminative orthonormal dictionary learning method is proposed for low rank representation with fast computation. Two kinds of discriminative information is used for learning the dictionary. Experiments on face recognition from both image and video demonstrate the effectiveness of the proposed method.