Generalizability theory
View article: PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space Open
Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to reco…
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Sex and Gender Equity in Research: rationale for the SAGER guidelines and recommended use Open
Background Sex and gender differences are often overlooked in research design, study implementation and scientific reporting, as well as in general science communication. This oversight limits the generalizability of research findings and …
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Artificial intelligence in cancer imaging: Clinical challenges and applications Open
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of dis…
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Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers Open
T he advent of deep neural networks as a new artifi- cial intelligence (AI) technique has engendered a large number of medical applications, particularly in medical imaging.Such applications of AI must remain grounded in the fundamental te…
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Generalizability in qualitative research: misunderstandings, opportunities and recommendations for the sport and exercise sciences Open
Generalisation in relation to qualitative research has rarely been discussed in-depth in sport and exercise psychology, the sociology of sport, sport coaching, or sport management journals. Often there is no mention of generalizability in …
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Lack of group-to-individual generalizability is a threat to human subjects research Open
Significance The current study quantified the degree to which group data are able to describe individual participants. We utilized intensive repeated-measures data—data that have been collected many times, across many individuals—to compar…
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Going deeper in facial expression recognition using deep neural networks Open
Automated Facial Expression Recognition (FER) has remained a challenging and\ninteresting problem. Despite efforts made in developing various methods for\nFER, existing approaches traditionally lack generalizability when applied to\nunseen…
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The Medical Segmentation Decathlon Open
International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organ…
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Artificial Intelligence in Dentistry: Chances and Challenges Open
The term “artificial intelligence” (AI) refers to the idea of machines being capable of performing human tasks. A subdomain of AI is machine learning (ML), which “learns” intrinsic statistical patterns in data to eventually cast prediction…
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Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey Open
Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in t…
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II. MORE THAN JUST CONVENIENT: THE SCIENTIFIC MERITS OF HOMOGENEOUS CONVENIENCE SAMPLES Open
Despite their disadvantaged generalizability relative to probability samples, nonprobability convenience samples are the standard within developmental science, and likely will remain so because probability samples are cost-prohibitive and …
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How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice Open
Multiplicative interaction models are widely used in social science to examine whether the relationship between an outcome and an independent variable changes with a moderating variable. Current empirical practice tends to overlook two imp…
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Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network Open
We propose a deep bilinear model for blind image quality assessment (BIQA)\nthat handles both synthetic and authentic distortions. Our model consists of\ntwo convolutional neural networks (CNN), each of which specializes in one\ndistortion…
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External validation of prognostic models: what, why, how, when and where? Open
Prognostic models that aim to improve the prediction of clinical events, individualized treatment and decision-making are increasingly being developed and published. However, relatively few models are externally validated and validation by…
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Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion Open
Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangemen…
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Implementation of subjective cognitive decline criteria in research studies Open
Introduction Subjective cognitive decline (SCD) manifesting before clinical impairment could serve as a target population for early intervention trials in Alzheimer's disease (AD). A working group, the Subjective Cognitive Decline Initiati…
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Plasma proteomic signature of age in healthy humans Open
To characterize the proteomic signature of chronological age, 1,301 proteins were measured in plasma using the SOMAscan assay (SomaLogic, Boulder, CO, USA) in a population of 240 healthy men and women, 22–93 years old, who were disease‐ an…
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Towards Environment Independent Device Free Human Activity Recognition Open
Driven by a wide range of real-world applications, significant efforts have recently been made to explore device-free human activity recognition techniques that utilize the information collected by various wireless infrastructures to infer…
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The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset Open
Kandel, I., & Castelli, M. (2020). The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express, 6(4), 312-315. https://doi.org/10.1016/j.icte.2020.04.010
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PotentialNet for Molecular Property Prediction Open
The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales. The key parameters range from solubility (angstroms) to protein-ligand binding (nanometers) to in vivo toxicity (meters). Through feature …
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Sampling methods in Clinical Research; an Educational Review. Open
Clinical research usually involves patients with a certain disease or a condition. The generalizability of clinical research findings is based on multiple factors related to the internal and external validity of the research methods. The m…
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How to do a systematic review Open
High quality up-to-date systematic reviews are essential in order to help healthcare practitioners and researchers keep up-to-date with a large and rapidly growing body of evidence. Systematic reviews answer pre-defined research questions …
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Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data Open
Acquiring resting‐state functional magnetic resonance imaging (fMRI) datasets at multiple MRI scanners and clinical sites can improve statistical power and generalizability of results. However, multi‐site neuroimaging studies have reported…
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Barriers to Clinical Trial Enrollment in Racial and Ethnic Minority Patients with Cancer Open
Background Clinical trials that study cancer are essential for testing the safety and effectiveness of promising treatments, but most people with cancer never enroll in a clinical trial — a challenge exemplified in racial and ethnic minori…
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Replicability and Generalizability of Posttraumatic Stress Disorder (PTSD) Networks: A Cross-Cultural Multisite Study of PTSD Symptoms in Four Trauma Patient Samples Open
The growing literature conceptualizing mental disorders like posttraumatic stress disorder (PTSD) as networks of interacting symptoms faces three key challenges. Prior studies predominantly used (a) small samples with low power for precise…
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Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics Open
This article offers a systematic analysis of 727 manuscripts that used Reddit as a data source, published between 2010 and 2020. Our analysis reveals the increasing growth in use of Reddit as a data source, the range of disciplines this re…
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Generalizability of heterogeneous treatment effect estimates across samples Open
Significance In experiments, the degree to which results generalize to other populations depends critically on the degree of treatment effect heterogeneity. We replicated 27 survey experiments (encompassing 101,745 individual survey respon…
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Psychometric Properties of the Difficulties in Emotion Regulation Scale (DERS) and Its Short Forms in Adults With Emotional Disorders Open
Objective: The Difficulties in Emotion Regulation Scale (DERS) is a widely used self-report measure of subjective emotion ability, as defined by a prominent clinically derived model of emotion regulation (Gratz and Roemer, 2004). Although …
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nnU-Net: Self-adapting Framework for U-Net-Based Medical Image\n Segmentation Open
The U-Net was presented in 2015. With its straight-forward and successful\narchitecture it quickly evolved to a commonly used benchmark in medical image\nsegmentation. The adaptation of the U-Net to novel problems, however, comprises\nseve…
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Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020 Open
Objective : Vast 12-lead ECGs repositories provide opportunities to develop new machine learning approaches for creating accurate and automatic diagnostic systems for cardiac abnormalities. However, most 12-lead ECG classification studies …