1,721,578 research outputs found
Diffusion MRI Allows Capturing the Amyloid-β and τ Proteins Status in Alzheimer’s Disease Continuum
No full textAlzheimer's Disease (AD) is a neurodegenerative process characterized by the accumulation of amyloid-β (Aβ) and tau (τ) proteins leading to neurodegeneration. It has been hypothesizes that the aggregation of these proteins could spread through specific white matter (WM) pathways. To investigate such hypothesis, convolutional neural networks were employed to analyze microstructural alterations induced by the accumulation of Aβ and τ proteins via two classification tasks, relying on mean diffusivity (MD) and fractional anisotropy (FA), achieving competitive performance. A post-hoc analysis via integrated gradients revealed that the splenium of the corpus callosum played a prominent role for both indices, while index-specific were the ventricles and subcortical regions for MD and the corticospinal tract for FA. This supports the assumption that WM pathways might play a key role in misfolded protein distribution and highlights the potential of diffusion imaging for the identification of Aβ and τ proteins spread and accumulation
Objective Assessment of the Bias Introduced by Baseline Signals in XAI Attribution Methods
No full textThis work represents a first step towards a systematic analysis of the impact of the choice of the baseline signals to be used in explainable baseline-dependent methods for multi-modal and multi-dimensional data relying on single-input deep networks, in view of the generalization to multi -channel architectures. This point is critical for ensuring the soundness of the attribution values and enabling their subsequent validation through association studies. In this work, two different CNNs were implemented to predict Alzheimer's disease patients from control subjects using structural Magnetic Resonance Imaging volumes and genetics data. The Integrated Gradients method was applied to both models for post-hoc attribution visualization relying on different baselines. Differences in the attribution maps were found with respect to the attributions of the reference baseline in both modalities highlighting the importance of finding and using the 'optimal' baseline. We believe this work is highly relevant for the community in the framework of the validation of XAI post-hoc methods, as it provides evidence of the impact of the choice of the baselines for deriving feature attribution values with the Integrated Gradients method which determines the reliability of the outcomes, improving both the awareness of the users and their trust in the methods
Explainable AI (XAI) In Biomedical Signal and Image Processing: Promises and Challenges
No full textArtificial intelligence has become pervasive across disciplines and fields, and biomedical image and signal processing is no exception. The growing and widespread interest on the topic has triggered a vast research activity that is reflected in an exponential research effort. Through study of massive and diverse biomedical data, machine and deep learning models have revolutionized various tasks such as modeling, segmentation, registration, classification and synthesis, outperforming traditional techniques. However, the difficulty in translating the results into biologically/clinically interpretable information is preventing their full exploitation in the field. Explainable AI (XAI) attempts to fill this translational gap by providing means to make the models interpretable and providing explanations. Different solutions have been proposed so far and are gaining increasing interest from the community. This paper aims at providing an overview on XAI in biomedical data processing and points to an upcoming Special Issue on Deep Learning in Biomedical Image and Signal Processing of the IEEE Signal Processing Magazine that is going to appear in March 2022
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation
No full textThe growth of data sharing initiatives for neuroimaging and genomics represents an exciting opportunity to confront the “small N” problem that plagues contemporary neuroimaging studies while further understanding the role genetic markers play in the function of the brain. When it is possible, open data sharing provides the most benefits. However, some data cannot be shared at all due to privacy concerns and/or risk of re-identification. Sharing other data sets is hampered by the proliferation of complex data use agreements (DUAs) which preclude truly automated data mining. These DUAs arise because of concerns about the privacy and confidentiality for subjects; though many do permit direct access to data, they often require a cumbersome approval process that can take months. An alternative approach is to only share data derivatives such as statistical summaries—the challenges here are to reformulate computational methods to quantify the privacy risks associated with sharing the results of those computations. For example, a derived map of gray matter is often as identifiable as a fingerprint. Thus alternative approaches to accessing data are needed. This paper reviews the relevant literature on differential privacy, a framework for measuring and tracking privacy loss in these settings, and demonstrates the feasibility of using this framework to calculate statistics on data distributed at many sites while still providing privacy.This document is protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission.Peer reviewe
Variations on the Author
Get PDF“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
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe 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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
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