varunr14/newsummary huggingface dataset:
Background and objective This study proposes an infrastructure with a reporting workflow optimization algorithm in order to interconnect facilities reporting units and radiologists on a single access interface to increase the efficiency of the reporting process by decreasing the medical report turnaround time and to increase the quality of medical reports by determining the optimum match between the inspection and radiologist in terms of subspecialty workload and response time . Methods Workflow centric network architecture with an enhanced caching querying and retrieving mechanism is implemented by seamlessly integrating Grid Agent and Grid Manager to conventional digital radiology systems . The inspection and radiologist attributes are modelled using a hierarchical ontology structure . Attribute preferences rated by radiologists and technical experts are formed into reciprocal matrixes and weights for entities are calculated utilizing Analytic Hierarchy Process . The assignment alternatives are processed by relation based semantic matching and Integer Linear Programming . Results The results are evaluated based on both real case applications and simulated process data in terms of subspecialty response time and workload success rates . Results obtained using simulated data are compared with the outcomes obtained by applying Round Robin Shortest Queue and Random distribution policies . The proposed algorithm is also applied to a real case teleradiology application process data where medical reporting workflow was performed based on manual assignments by the chief radiologist for 6225 inspections . Conclusions RBSM gives the highest subspecialty success rate and integrating ILP with RBSM ratings as RWOA provides a better response time and workload distribution success rate . RWOA based image delivery also prevents bandwidth storage or hardware related stuck and latencies . When compared with a real case teleradiology application where inspection assignments were performed manually the proposed solution was found to increase the experience success rate by 13.25 workload success rate by 63.76 and response time success rate by 120 . The total response time in the real case application data was improved by 22.39 . 


Summary: We propose a workflow centric network architecture for teleradiology applications. We optimize inspection assignments to radiologists for medical reporting process. Subspecialty response time and workload parameters are improved for assignments. Solution gives better results for workflow efficiency compared to manual assignment. Architecture was tested in 52 sites and 3.35 million inspections were processed.
The human visual system is quite adept at swiftly detecting objects of interest in complex visual scene . Simulating human visual system to detect visually salient regions of an image has been one of the active topics in computer vision . Inspired by random sampling based bagging ensemble learning method an ensemble dictionary learning framework for saliency detection is proposed in this paper . Instead of learning a universal dictionary requiring a large number of training samples to be collected from natural images multiple over complete dictionaries are independently learned with a small portion of randomly selected samples from the input image itself resulting in more flexible multiple sparse representations for each of the image patches . To boost the distinctness of salient patch from background region we present a reconstruction residual based method for dictionary atom reduction . Meanwhile with the obtained multiple probabilistic saliency responses for each of the patches the combination of them is finally carried out from the probabilistic perspective to achieve better predictive performance on saliency region . Experimental results on several open test datasets and some natural images demonstrate that the proposed EDL for saliency detection is much more competitive compared with some existing state of the art algorithms . 


Summary: We propose an ensemble dictionary learning EDL framework for saliency detection. The saliency detection within this framework is treated as a novelty detection problem. A novel dictionary atom reduction is proposed for boosting the distinctness of salient region. A good probabilistic interpretation is with the proposed EDL model.
3D shape descriptor has been used widely in the field of 3D object retrieval . However the performance of object retrieval greatly depends on the shape descriptor used . The aims of this study is to review and compare the common 3D shape descriptors proposed in 3D object retrieval literature for object recognition and classification based on Kinect like depth image obtained from RGB D object dataset . In this paper we introduce inter class and intra class evaluation in order to study the feasibility of such descriptors in object recognition . Based on these evaluations local spin image outperforms the rest in discriminating different classes when several depth images from an instance per class are used in inter class evaluation . This might be due to the slightly consistent local shape property of such images and due to the proposed local similarity measurement that manages to extract the local based descriptor . However shape distribution performs excellent for intra class evaluation may be due to the global shape from different instances per class is slightly unchanged . These results indicate a remarkable feasibility analysis of the 3D shape descriptor in object recognition that can be potentially used for Kinect like sensor . 


Summary: We model the common 3D shape descriptor from Kinect like depth image. We evaluate the 3D shape descriptor in object recognition. We introduce 3D shape descriptor performance frameworks. Shape distribution and local spin image outperformed other 3D shape descriptors.
Despite the successes in the last two decades the state of the art face detectors still have problems in dealing with images in the wild due to large appearance variations . Instead of leaving appearance variations directly to statistical learning algorithms we propose a hierarchical part based structural model to explicitly capture them . The model enables part subtype option to handle local appearance variations such as closed and open month and part deformation to capture the global appearance variations such as pose and expression . In detection candidate window is fitted to the structural model to infer the part location and part subtype and detection score is then computed based on the fitted configuration . In this way the influence of appearance variation is reduced . Besides the face model we exploit the co occurrence between face and body which helps to handle large variations such as heavy occlusions to further boost the face detection performance . We present a phrase based representation for body detection and propose a structural context model to jointly encode the outputs of face detector and body detector . Benefit from the rich structural face and body information as well as the discriminative structural learning algorithm our method achieves state of the art performance on FDDB AFW and a self annotated dataset under wide comparisons with commercial and academic methods . 


Summary: We enrich face detection model by hierarchical structure and part subtype. We propose to explore the face body co occurrence to improve face detection. We achieve state of the art performance on FDDB AFW and a self annotated dataset.
Background and objectives The lack of benchmark data in computational ophthalmology contributes to the challenging task of applying disease assessment and evaluate performance of machine learning based methods on retinal spectral domain optical coherence tomography scans . Presented here is a general framework for constructing a benchmark dataset for retinal image processing tasks such as cyst vessel and subretinal fluid segmentation and as a result a benchmark dataset for cyst segmentation has been developed . Method First a dataset captured by different SD OCT vendors with different numbers of scans and pathology qualities are selected . Then a robust and intelligent method is used to evaluate performance of readers partitioning the dataset into subsets . Subsets are then assigned to complementary readers for annotation with respect to a novel confidence based annotation protocol . Finally reader annotations are combined based on their performance to generate final annotations . Result The generated benchmark dataset for cyst segmentation comprises 26 SD OCT scans with differing cyst qualities collected from 4 different SD OCT vendors to cover a wide variety of data . The dataset is partitioned into three subsets which are annotated by complementary readers based on a confidence based annotation protocol . Experimental results show annotations of complementary readers are combined efficiently with respect to their performance generating accurate annotations . Conclusion Our results facilitate the process of generating benchmark datasets . Moreover the generated benchmark data set for cyst segmentation can be used reliably to train and test machine learning based methods . 


Summary: A framework including data selection task assignment and annotation combination stages for a confidence based benchmark dataset for retinal image processing is proposed. A novel task assignment is used to remove data and reader biases. The annotation of readers is combined based on their accuracy and performance. The framework is used to build a confidence based benchmark dataset for cyst segmentation. The generated benchmark can be used to reliably evaluate cyst segmentation methods.
This paper focuses on activity recognition when multiple views are available. In the literature this is often performed using two different approaches. In the first one the systems build a 3D reconstruction and match that. However there are practical disadvantages to this methodology since a sufficient number of overlapping views is needed to reconstruct and one must calibrate the cameras. A simpler alternative is to match the frames individually. This offers significant advantages in the system architecture e.g. it is easy to incorporate new features and camera dropouts can be tolerated . In this paper the second approach is employed and a novel fusion method is proposed. Our fusion method collects the activity labels over frames and cameras and then fuses activity judgments as the sequence label. It is shown that there is no performance penalty when a straightforward weighted voting scheme is used. In particular when there are enough overlapping views to generate a volumetric reconstruction our recognition performance is comparable with that produced by volumetric reconstructions. However if the overlapping views are not adequate the performance degrades fairly gracefully even in cases where test and training views do not overlap. 


Summary: Data fusion based method for activity recognition using multiple views Straightforward architecture to incorporate new cameras or new features Performance generally increases when there are more cameras and features. Comparable performance with that produced by reconstruction Detailed experiments to answer different system considerations
Objective The analysis of treatment effects in clinical trials usually focus on efficacy and safety in separate descriptive statistical analyses . The Q TWiST method has been proposed by Gelber in the 90s to enable a statistical comparison between two groups with a graphical representation by incorporating benefit and risk into a single analysis . Although the method has been programmed in SAS it is rarely used . The availability of the method in the freely software environment system like R would greatly enhanced the accessibility by researchers . The objective of this paper is to present a program for Q TWiST analyses within R software environment . Methods The qtwist function was developed in order to estimate and compare Q TWiST for two groups . Two individual patient data files are required used for input one for visits and one for follow up . Q TWiST is obtained as a sum of time spent in three health states period in toxicity period without relapse and toxicity and period in relapse weighted by associated utility scores restricted to median overall survival for example . The bootstrap method is used for testing statistical significance . Threshold analysis and gain functions allow a group comparison for different utility values . Results Input data is checked for consistency . Descriptive statistics and mean durations for each health state are provided allowing statistical comparisons . Graphical results are presented in a PDF file . The use of the function is illustrated with data from a simulated data set and a randomized clinical trial . Conclusions qtwist is an easy to use R function allowing a quality adjusted survival analysis with the Q TWiST method . 


Summary: We developed a function written the R software performing Q TWiST analyses. Three health states are defined toxicity relapse and TWiST. Inputs are checked and descriptive comparative and graphical results are provided. Several arguments take the opportunity to add more options to the analyze.
A method to obtain accurate hand gesture classification and fingertip localization from depth images is proposed . The Oriented Radial Distribution feature is utilized exploiting its ability to globally describe hand poses but also to locally detect fingertip positions . Hence hand gesture and fingertip locations are characterized with a single feature calculation . We propose to divide the difficult problem of locating fingertips into two more tractable problems by taking advantage of hand gesture as an auxiliary variable . Along with the method we present the ColorTip dataset a dataset for hand gesture recognition and fingertip classification using depth data . ColorTip contains sequences where actors wear a glove with colored fingertips allowing automatic annotation . The proposed method is evaluated against recent works in several datasets achieving promising results in both gesture classification and fingertip localization . 


Summary: A novel use of the ORD feature to both globally and locally characterize hands A dataset for hand gesture classification and fingertip localization is proposed. We reduce the search space of fingertip locations conditioned to a hand gesture. A new cost function to solve the graph matching problem of fingertip localization
Background and Objective Iterative reconstruction from Compton scattered data is known to be computationally more challenging than that from conventional line projection based emission data in that the gamma rays that undergo Compton scattering are modeled as conic projections rather than line projections . In conventional tomographic reconstruction to parallelize the projection and backprojection operations using the graphics processing unit approximated methods that use an unmatched pair of ray tracing forward projector and voxel driven backprojector have been widely used . In this work we propose a new GPU accelerated method for Compton camera reconstruction which is more accurate by using exactly matched pair of projector and backprojector . Methods To calculate conic forward projection we first sample the cone surface into conic rays and accumulate the intersecting chord lengths of the conic rays passing through voxels using a fast ray tracing method . For conic backprojection to obtain the true adjoint of the conic forward projection while retaining the computational efficiency of the GPU we use a voxel driven RTM which is essentially the same as the standard RTM used for the conic forward projector . Results Our simulation results show that while the new method is about 3 times slower than the approximated method it is still about 16 times faster than the CPU based method without any loss of accuracy . Conclusions The net conclusion is that our proposed method is guaranteed to retain the reconstruction accuracy regardless of the number of iterations by providing a perfectly matched projector backprojector pair which makes iterative reconstruction methods for Compton imaging faster and more accurate . 


Summary: Acceleration of iterative Compton camera reconstruction using graphics processing unit. Exactly matched pair of conic projector and backprojector. Significantly reduced reconstruction time without loss of accuracy. Improvement of reconstruction accuracy in advanced iterative methods.
In this paper initially the impact of mask spoofing on face recognition is analyzed . For this purpose one baseline technique is selected for both 2D and 3D face recognition . Next novel countermeasures which are based on the analysis of different shape texture and reflectance characteristics of real faces and mask faces are proposed to detect mask spoofing . In this paper countermeasures are developed using both 2D data and 3D data available in the mask database . The results show that each of the proposed countermeasures is successful in detecting mask spoofing and the fusion of these countermeasures further improves the results compared to using a single countermeasure . Since there is no publicly available mask database studies on mask spoofing are limited . This paper provides significant results by proposing novel countermeasures to protect face recognition systems against mask spoofing . 


Summary: 2D and 3D face recognition systems are vulnerable to mask spoofing. Countermeasures are proposed based on reflectance texture and depth analysis. Reflectance analysis provides best performance to detect 3D mask attacks. Fusion of countermeasures increases the mask spoofing detection performance. Classification accuracy of 99 almost perfect is reached to detect mask attacks.
Finding regions of interest is a fundamentally important problem in the area of computer vision and image processing . Previous studies addressing this issue have mainly focused on investigating chromatic cues to characterize visually salient image regions while less attention has been devoted to monochromatic cues . The purpose of this paper is the study of monochromatic cues which have the potential to complement chromatic cues for the detection of ROIs in an image . This paper first presents a taxonomy of existing ROI detection approaches using monochromatic cues ranging from well known algorithms to the most recently published techniques . We then propose a novel monochromatic cue for ROI detection . Finally a comparative evaluation has been conducted on large scale challenging test sets of real world natural scenes . Experimental results demonstrate that the use of our proposed monochromatic cue yields a more accurate identification of ROIs . This paper serves as a benchmark for future research on this particular topic and a steppingstone for developers and practitioners interested in adopting monochromatic cues to ROI detection systems and methodologies . 


Summary: We study monochromatic cues in the modeling of bottom up attention. We propose a novel monochromatic cue for ROI detection. Experimental results demonstrate the effectiveness of our method.
Concurrently obtaining an accurate robust and fast global registration of multiple 3D scans is still an open issue for modern 3D modeling pipelines especially when high metric precision as well as easy usage of high end devices structured light or laser scanners are required. Various solutions have been proposed either heuristic iterative and or closed form solutions which present some compromise concerning the fulfillment of the above contrasting requirements. Our purpose here compared to existing reference solutions is to go a step further in this perspective by presenting a new technique able to provide improved alignment performance even on large datasets both in terms of number of views and or point density of range images. Relying on the Optimization on a Manifold OOM approach originally proposed by Krishnan et al. we propose a set of methodological and computational upgrades that produce an operative impact on both accuracy robustness and computational performance compared to the original solution. In particular always basing on an unconstrained error minimization over the manifold of rotations instead of relying on a static set of point correspondences our algorithm updates the optimization iterations with a dynamically modified set of correspondences in a computationally effective way leading to substantial improvements in terms of registration accuracy and convergence trend. Other proposed improvements are directed to a substantial reduction of the computational load without sacrificing the alignment performance. Stress tests with increasing view misalignment allowed us to appreciate the convergence robustness of the proposed solution. Eventually we demonstrate that for very large datasets a further computational speedup can be reached by the adoption of a hybrid local heuristic followed by global optimization registration approach. 


Summary: Goal new approach to enable global registration of large collections of point sets. We consider an optimization on a manifold for global registration of multiple scans. We evidence computational and convergence issues in the original approach. We propose computationally effective correspondence update and other improvements. Results better accuracy compared to state of the art good computational performance.
An extensive in depth study of cardiovascular risk factors seems to be of crucial importance in the research of cardiovascular disease in order to prevent the chance of developing or dying from CVD . The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF . In this paper a report on applying Bayesian network modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years cardiovascular risk score and metabolic syndrome is presented . Furthermore the induced BN was used to make inference taking into account three reasoning patterns causal reasoning evidential reasoning and intercausal reasoning . Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF . The BN analysis showed several interesting results among them CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY MetS was highly influence by physical activity being again the group of men the one with highest risk in MetS and smoking did not show any influence . BNs produce an intuitive transparent graphical representation of the relationships between different CVRF . The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence tool of special interest in epidemiological studies . As CVD is multifactorial the use of BNs seems to be an adequate modeling tool . 


Summary: An epidemiologic system analysis of cardiovascular risk is presented through a Bayesian network model. The Bayesian network model can serve as a generic tool for application oriented activities explanation prediction monitoring and prevention. Due to cardiovascular disease is multifactorial the application of this kind of model is of special interest both from theoretical and practical point of view. The induced Bayesian network was used to make inferences taking into account three reasoning patterns causal reasoning evidential reasoning and intercausal reasoning.