1,721,337 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Structure-guided registration in learning based image analysis
Full text linkImage registration is a key component in many medical image analysis pipelines and is useful in general computer vision applications. The goal of image registration is to find a transformation between the coordinate spaces of two images, such that the transformation aligns some Structure-of-Interest which exist in both images. Object tracking, image segmentation, multi-modal data fusion, longitudinal studies, label propagation, image labelling, population studies, image stitching and voxel based morphometry either rely on or at least benefit from image registration.
In this thesis, three aspects of image registration are discussed. Firstly, we utilise image registration to perform image segmentation via template deformation, the registration of some prior shape model with an image. We utilise neural networks to perform this template registration, the networks implicitly embed Structure-of-Interest information during training, to utilise this during inference when Structure-of-Interest information is not readily available. This differs from the conventional template deformation paradigm, where one must construct some image to segmentation likelihood function for the registration algorithm, a proxy function for the true segmentation accuracy. Utilising neural networks circumvents having to do this, we are able to train a network directly using a segmentation loss without hand crafting such a loss function. Our method gives us the prior enforcing benefits of template deformations without the difficulty of deriving some approximation to the segmentation loss.
Secondly, we develop a framework for combining iterative image registration with neural network based representation learning. Recent network based image registration has generally focused on improving the speed of registration, as neural networks are able to predict deformation fields in one shot, rather than iteratively converging during test time. We argue however that this comes at the cost of the accuracy of the registration. We propose a method that extracts out a feature representation that is well suited to be registered by any downstream registration algorithm. This exploits the ability of neural networks to discover rich representations of data and combining it with all the strengths of traditional registration algorithms.
Thirdly, we show why image registration is useful, we introduce an algorithm which extends Gaussian processes such that they can be successfully applied to large scale, 3D medical data. Gaussian processes are not generally well suited to image data, as computing covariances between image pixels is often nonsensical, unless however images are registered. By utilising registration, we are able to introduce patch kernels to allow for more anatomically dependent covariances between images.
Finally, we introduce two interesting new topics of research which we believe have relations and applications to image registration in a bid to further fuel discussion and investigation in the field.Open Acces
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping
No full textTese de mestrado integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), 2021, Universidade de Lisboa, Faculdade de CiênciasThe assessment of Computed Tomography (CT) scans for Traumatic Brain Injury (TBI) management remains a time consuming and challenging task for physicians. Computational methods for quantitative lesion segmentation and localisation may increase consistency in diagnosis and prognosis criteria.
Our goal was to develop a registration-based tool to accurately localise several lesion classes (i.e., calculate the volume of lesion per brain region), as an extension of the Brain Lesion Analysis and Segmentation Tool for CT (BLAST-CT).
Lesions were located by projecting a Magnetic Resonance Imaging (MRI) labelled atlas from the
Montreal Neurological Institute (MNI MRI atlas) to a lesion map in native space. We created a CT
template to work as an intermediate step between the two imaging spaces, using 182 non-lesioned CT
scans and an unbiased iterative registration approach. We then non-linearly registered the parcellated
atlas to the CT template, subsequently registering (affine) the result to native space. From the final atlas
alignment, it was possible to calculate the volume of each lesion class per brain region. This pipeline
was validated on a multi-centre dataset (n=839 scans), and defined three methods to flag any scans that
presented sub-optimal results. The first one was based on the similarity metric of the registration of every
scan to the study-specific CT template, the second aimed to identify any scans with regions that were
completely collapsed post registration, and the final one identified scans with a significant volume of
intra-ventricular haemorrhage outside of the ventricles. Additionally, an assessment of lesion prevalence
and of the false negative and false positive rates of the algorithm, per anatomical region, was conducted,
along with a bias assessment of the BLAST-CT tool.
Our results show that the constructed pipeline is able to successfully localise TBI lesions across
the whole brain, although without voxel-wise accuracy. We found the error rates calculated for each
brain region to be inversely correlated with the lesion volume within that region. No considerable bias
was identified on BLAST-CT, as all the significant correlation coefficients calculated between the Dice
scores and clinical variables (i.e., age, Glasgow Coma Scale, Extended Glasgow Outcome Scale and
Injury Severity Score) were below 0.2. Our results also suggest that the variation in DSC between male
and female patients within a specific age range was caused by the discrepancy in lesion volume presented
by the scans included in each sample
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping
No full textTese de mestrado integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), 2021, Universidade de Lisboa, Faculdade de CiênciasThe assessment of Computed Tomography (CT) scans for Traumatic Brain Injury (TBI) management remains a time consuming and challenging task for physicians. Computational methods for quantitative lesion segmentation and localisation may increase consistency in diagnosis and prognosis criteria.
Our goal was to develop a registration-based tool to accurately localise several lesion classes (i.e., calculate the volume of lesion per brain region), as an extension of the Brain Lesion Analysis and Segmentation Tool for CT (BLAST-CT).
Lesions were located by projecting a Magnetic Resonance Imaging (MRI) labelled atlas from the
Montreal Neurological Institute (MNI MRI atlas) to a lesion map in native space. We created a CT
template to work as an intermediate step between the two imaging spaces, using 182 non-lesioned CT
scans and an unbiased iterative registration approach. We then non-linearly registered the parcellated
atlas to the CT template, subsequently registering (affine) the result to native space. From the final atlas
alignment, it was possible to calculate the volume of each lesion class per brain region. This pipeline
was validated on a multi-centre dataset (n=839 scans), and defined three methods to flag any scans that
presented sub-optimal results. The first one was based on the similarity metric of the registration of every
scan to the study-specific CT template, the second aimed to identify any scans with regions that were
completely collapsed post registration, and the final one identified scans with a significant volume of
intra-ventricular haemorrhage outside of the ventricles. Additionally, an assessment of lesion prevalence
and of the false negative and false positive rates of the algorithm, per anatomical region, was conducted,
along with a bias assessment of the BLAST-CT tool.
Our results show that the constructed pipeline is able to successfully localise TBI lesions across
the whole brain, although without voxel-wise accuracy. We found the error rates calculated for each
brain region to be inversely correlated with the lesion volume within that region. No considerable bias
was identified on BLAST-CT, as all the significant correlation coefficients calculated between the Dice
scores and clinical variables (i.e., age, Glasgow Coma Scale, Extended Glasgow Outcome Scale and
Injury Severity Score) were below 0.2. Our results also suggest that the variation in DSC between male
and female patients within a specific age range was caused by the discrepancy in lesion volume presented
by the scans included in each sample
- …
