Academic Digital Library for institutions, students, and solo learners

Discovery

Content search and filters

This is the first search surface for the seven content types. Next we will connect full-text search and metadata-specific filters.

ACTIVE FILTERS:Dept: Architecture & Planning Clear All

Results 1,238

in Architecture & Planning
Periodicals

Deep Residual Learning for Image Recognition: A Survey

Deep Residual Networks have recently been shown to significantly improve the performance of neural networks trained on ImageNet, with results beating all previous methods on this dataset by large margins in the image classification task. However, the meaning of these impressive numbers and their implications for future research are not fully understood yet. In this survey, we will try to explain what Deep Residual Networks are, how they achieve their excellent results, and why their successful implementation in practice represents a significant advance over existing techniques. We also discuss some open questions related to residual learning as well as possible applications of Deep Residual Networks beyond ImageNet. Finally, we discuss some issues that still need to be resolved before deep residual learning can be applied on more complex problems.

Architecture & Planning• 2022• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

A Survey on the Explainability of Supervised Machine Learning

Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or finance, is of paramount importance. The decision-making behind the black boxes requires it to be more transparent, accountable, and understandable for humans. This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning (SML). We conduct a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions. Finally, we illustrate principles by means of an explanatory case study and discuss important future directions.

Architecture & Planning• 2021• AI Access Foundation
Access Full TextView Details
Periodicals

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering the rapid evolution of this field, this article provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers. To be specific, we discuss the main challenges of remote sensing image scene classification and survey: first, autoencoder-based remote sensing image scene classification methods; second, convolutional neural network-based remote sensing image scene classification methods; and third, generative adversarial network-based remote sensing image scene classification methods. In addition, we introduce the benchmarks used for remote sensing image scene classification and summarize the performance of more than two dozen of representative algorithms on three commonly used benchmark datasets. Finally, we discuss the promising opportunities for further research.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
Access Full TextView Details
Periodicals

Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS

Remote sensing is one of the tool which is very important for the production of Land use and land cover maps through a process called image classification. For the image classification process to be successfully, several factors should be considered including availability of quality Landsat imagery and secondary data, a precise classification process and user’s experiences and expertise of the procedures. The objective of this research was to classify and map land-use/land-cover of the study area using remote sensing and Geospatial Information System (GIS) techniques. This research includes two sections (1) Landuse/Landcover (LULC) classification and (2) accuracy assessment. In this study supervised classification was performed using Non Parametric Rule. The major LULC classified were agriculture (65.0%), water body (4.0%), and built up areas (18.3%), mixed forest (5.2%), shrubs (7.0%), and Barren/bare land (0.5%). The study had an overall classification accuracy of 81.7% and kappa coefficient (K) of 0.722. The kappa coefficient is rated as substantial and hence the classified image found to be fit for further research. This study present essential source of information whereby planners and decision makers can use to sustainably plan the environment.

Architecture & Planning• 2017• Scientific Research Publishing
Access Full TextView Details
Periodicals

In-Kind Finance: A Theory of Trade Credit

It is typically less profitable for an opportunistic borrower to divert inputs than to divert cash. Therefore, suppliers may lend more liberally than banks. This simple argument is at the core of our contract theoretic model of trade credit in competitive markets. The model implies that trade credit and bank credit can be either complements or substitutes. Among other things, the model explains why trade credit has short maturity, why trade credit is more prevalent in less developed credit markets, and why accounts payable of large unrated firms are more countercyclical than those of small firms.

Architecture & Planning• 2004• American Economic Association
Access Full TextView Details
Periodicals

Plant Disease Detection and Classification by Deep Learning—A Review

Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

Architecture & Planning• 2021• Institute of Electrical and Electronics Engineers
Access Full TextView Details
Periodicals

Hydrogen Storage for Mobility: A Review

Numerous reviews on hydrogen storage have previously been published. However, most of these reviews deal either exclusively with storage materials or the global hydrogen economy. This paper presents a review of hydrogen storage systems that are relevant for mobility applications. The ideal storage medium should allow high volumetric and gravimetric energy densities, quick uptake and release of fuel, operation at room temperatures and atmospheric pressure, safe use, and balanced cost-effectiveness. All current hydrogen storage technologies have significant drawbacks, including complex thermal management systems, boil-off, poor efficiency, expensive catalysts, stability issues, slow response rates, high operating pressures, low energy densities, and risks of violent and uncontrolled spontaneous reactions. While not perfect, the current leading industry standard of compressed hydrogen offers a functional solution and demonstrates a storage option for mobility compared to other technologies.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

Prediction of Daily Climate Using Long Short-Term Memory (LSTM) Model

Climaate prediction plays a vital role in various sectors, including agriculture, disaster management, and urban planning. Traditional methods for climate forecasting often rely on complex physical models, which require substantial computational resources and may not accurately capture local weather patterns. This study explores the potential of Long Short-Term Memory (LSTM) networks, a type of recurrent neural network, for predicting daily climate variables such as temperature, precipitation, and humidity. Utilizing historical climate data from the city of Delhi, we developed an LSTM model to forecast short-term climate trends. The model consists of two LSTM layers followed by three Dense layers and is compiled with the Adam optimizer, mean squared error loss, and mean absolute error as a metric. Our results demonstrate the model's capability to capture temporal dependencies in climate data, achieving a satisfactory level of accuracy in temperature forecasting. This research underscores the potential of machine learning techniques, particularly LSTM networks, in enhancing climate prediction and contributing to more informed decision-making in weather-sensitive sectors.

Architecture & Planning• 2024
Access Full TextView Details
Periodicals

Digital Twin: Origin to Future

Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology.

Architecture & Planning• 2021• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

fNIRS-based brain-computer interfaces: a review

A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis (ICA), multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine (SVM), hidden Markov model (HMM), artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.

Architecture & Planning• 2015• Frontiers Media
Access Full TextView Details
Periodicals

VIRMA mediates preferential m6A mRNA methylation in 3′UTR and near stop codon and associates with alternative polyadenylation

Abstract N 6 -methyladenosine (m 6 A) is enriched in 3′untranslated region (3′UTR) and near stop codon of mature polyadenylated mRNAs in mammalian systems and has regulatory roles in eukaryotic mRNA transcriptome switch. Significantly, the mechanism for this modification preference remains unknown, however. Herein we report a characterization of the full m 6 A methyltransferase complex in HeLa cells identifying METTL3/METTL14/WTAP/VIRMA/HAKAI/ZC3H13 as the key components, and we show that VIRMA mediates preferential mRNA methylation in 3′UTR and near stop codon. Biochemical studies reveal that VIRMA recruits the catalytic core components METTL3/METTL14/WTAP to guide region-selective methylations. Around 60% of VIRMA mRNA immunoprecipitation targets manifest strong m 6 A enrichment in 3′UTR. Depletions of VIRMA and METTL3 induce 3′UTR lengthening of several hundred mRNAs with over 50% targets in common. VIRMA associates with polyadenylation cleavage factors CPSF5 and CPSF6 in an RNA-dependent manner. Depletion of CPSF5 leads to significant shortening of 3′UTR of over 2800 mRNAs, 84% of which are modified with m 6 A and have increased m 6 A peak density in 3′UTR and near stop codon after CPSF5 knockdown. Together, our studies provide insights into m 6 A deposition specificity in 3′UTR and its correlation with alternative polyadenylation.

Architecture & Planning• 2018• Springer Nature
Access Full TextView Details
Periodicals

An Overview of Autism Spectrum Disorder, Heterogeneity and Treatment Options

Since the documented observations of Kanner in 1943, there has been great debate about the diagnoses, the sub-types, and the diagnostic threshold that relates to what is now known as autism spectrum disorder (ASD). Reflecting this complicated history, there has been continual refinement from DSM-III with 'Infantile Autism' to the current DSM-V diagnosis. The disorder is now widely accepted as a complex, pervasive, heterogeneous condition with multiple etiologies, sub-types, and developmental trajectories. Diagnosis remains based on observation of atypical behaviors, with criteria of persistent deficits in social communication and restricted and repetitive patterns of behavior. This review provides a broad overview of the history, prevalence, etiology, clinical presentation, and heterogeneity of ASD. Factors contributing to heterogeneity, including genetic variability, comorbidity, and gender are reviewed. We then explore current evidence-based pharmacological and behavioral treatments for ASD and highlight the complexities of conducting clinical trials that evaluate therapeutic efficacy in ASD populations. Finally, we discuss the potential of a new wave of research examining objective biomarkers to facilitate the evaluation of sub-typing, diagnosis, and treatment response in ASD.

Architecture & Planning• 2017• Springer Science+Business Media
Access Full TextView Details
Periodicals

Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications

The development of energy management tools for next-generation PhotoVoltaic (PV) installations, including storage units, provides flexibility to distribution system operators. In this paper, the aggregation and implementation of these determinist energy management methods for business customers in a microgrid power system are presented. This paper proposes a determinist energy management system for a microgrid, including advanced PV generators with embedded storage units and a gas microturbine. The system is organized according to different functions and is implemented in two parts: a central energy management of the microgrid and a local power management at the customer side. The power planning is designed according to the prediction for PV power production and the load forecasting. The central and local management systems exchange data and order through a communication network. According to received grid power references, additional functions are also designed to manage locally the power flows between the various sources. Application to the case of a hybrid supercapacitor battery-based PV active generator is presented.

Architecture & Planning• 2011• Institute of Electrical and Electronics Engineers
Access Full TextView Details
Periodicals

Embodied energy and carbon in construction materials

The development of an open-access, reliable database for embodied energy and carbon (dioxide) emissions associated with the construction industry is described. The University of Bath's inventory of carbon and energy database lists almost 200 different materials. The data were extracted from peer-reviewed literature on the basis of a defined methodology and a set of five criteria. The database was made publicly available via an online website and has attracted significant interest from industry, academia, government departments and agencies, among others. Feedback from such professional users has played an important part in the choice of ‘best values’ for ‘cradleto-site’ embodied energy and carbon from the range found in the literature. The variation in published data stems from differences in boundary definitions (including geographic origin), age of the data sources and rigour of the original life-cycle assessments. Although principally directed towards UK construction, the material set included in the database is of quite wide application across the industrial sector. The use of the inventory is illustrated with the aid of 14 case studies of real-world new-build dwellings. It was observed that there was little difference between embodied energy and carbon for houses and apartments until external works were taken into account (energy inputs for roads, connecting pathways, etc.).

Architecture & Planning• 2008
Access Full TextView Details
Periodicals

Incidence, co-occurrence, and evolution of long-COVID features: A 6-month retrospective cohort study of 273,618 survivors of COVID-19

BACKGROUND: Long-COVID refers to a variety of symptoms affecting different organs reported by people following Coronavirus Disease 2019 (COVID-19) infection. To date, there have been no robust estimates of the incidence and co-occurrence of long-COVID features, their relationship to age, sex, or severity of infection, and the extent to which they are specific to COVID-19. The aim of this study is to address these issues. METHODS AND FINDINGS: We conducted a retrospective cohort study based on linked electronic health records (EHRs) data from 81 million patients including 273,618 COVID-19 survivors. The incidence and co-occurrence within 6 months and in the 3 to 6 months after COVID-19 diagnosis were calculated for 9 core features of long-COVID (breathing difficulties/breathlessness, fatigue/malaise, chest/throat pain, headache, abdominal symptoms, myalgia, other pain, cognitive symptoms, and anxiety/depression). Their co-occurrence network was also analyzed. Comparison with a propensity score-matched cohort of patients diagnosed with influenza during the same time period was achieved using Kaplan-Meier analysis and the Cox proportional hazard model. The incidence of atopic dermatitis was used as a negative control. Among COVID-19 survivors (mean [SD] age: 46.3 [19.8], 55.6% female), 57.00% had one or more long-COVID feature recorded during the whole 6-month period (i.e., including the acute phase), and 36.55% between 3 and 6 months. The incidence of each feature was: abnormal breathing (18.71% in the 1- to 180-day period; 7.94% in the 90- to180-day period), fatigue/malaise (12.82%; 5.87%), chest/throat pain (12.60%; 5.71%), headache (8.67%; 4.63%), other pain (11.60%; 7.19%), abdominal symptoms (15.58%; 8.29%), myalgia (3.24%; 1.54%), cognitive symptoms (7.88%; 3.95%), and anxiety/depression (22.82%; 15.49%). All 9 features were more frequently reported after COVID-19 than after influenza (with an overall excess incidence of 16.60% and hazard ratios between 1.44 and 2.04, all p < 0.001), co-occurred more commonly, and formed a more interconnected network. Significant differences in incidence and co-occurrence were associated with sex, age, and illness severity. Besides the limitations inherent to EHR data, limitations of this study include that (i) the findings do not generalize to patients who have had COVID-19 but were not diagnosed, nor to patients who do not seek or receive medical attention when experiencing symptoms of long-COVID; (ii) the findings say nothing about the persistence of the clinical features; and (iii) the difference between cohorts might be affected by one cohort seeking or receiving more medical attention for their symptoms. CONCLUSIONS: Long-COVID clinical features occurred and co-occurred frequently and showed some specificity to COVID-19, though they were also observed after influenza. Different long-COVID clinical profiles were observed based on demographics and illness severity.

Architecture & Planning• 2021• Public Library of Science
Access Full TextView Details
Periodicals

Multifunctional metal–organic frameworks: from academia to industrial applications

After three decades of intense and fundamental research on metal-organic frameworks (MOFs), is there anything left to say or to explain? The synthesis and properties of MOFs have already been comprehensively described elsewhere. It is time, however, to prove the nature of their true usability: technological applications based on these extended materials require development and implementation as a natural consequence of the up-to-known intensive research focused on their design and preparation. The current large number of reviews on MOFs emphasizes practical strategies to develop novel networks with varied crystal size, shape and topology, being mainly devoted to academic concerns. The present survey intends to push the boundaries and summarise the state-of-the-art on the preparation of promising (multi)functional MOFs in worldwide laboratories and their use as materials for industrial implementation. This review starts, on the one hand, to describe several tools and striking examples of remarkable and recent (multi)functional MOFs exhibiting outstanding properties (e.g., in gas adsorption and separation, selective sorption of harmful compounds, heterogeneous catalysis, luminescent and corrosion protectants). On the other hand, and in a second part, it intends to use these examples of MOFs to incite scientists to move towards the transference of knowledge from the laboratories to the industry. Within this context, we exhaustively review the many efforts of several worldwide commercial companies to bring functional MOFs towards the daily use, analysing the various patents and applications reported to date. Overall, this review goes from the very basic concepts of functional MOF engineering and preparation ending up in their industrial production on a large scale and direct applications in society.

Architecture & Planning• 2015• Royal Society of Chemistry
Access Full TextView Details
Periodicals

Blockchain for AI: Review and Open Research Challenges

Recently, artificial intelligence (AI) and blockchain have become two of the most trending and disruptive technologies. Blockchain technology has the ability to automate payment in cryptocurrency and to provide access to a shared ledger of data, transactions, and logs in a decentralized, secure, and trusted manner. Also with smart contracts, blockchain has the ability to govern interactions among participants with no intermediary or a trusted third party. AI, on the other hand, offers intelligence and decision-making capabilities for machines similar to humans. In this paper, we present a detailed survey on blockchain applications for AI. We review the literature, tabulate, and summarize the emerging blockchain applications, platforms, and protocols specifically targeting AI area. We also identify and discuss open research challenges of utilizing blockchain technologies for AI.

Architecture & Planning• 2019• Institute of Electrical and Electronics Engineers
Access Full TextView Details
Periodicals

Are we ready for SDN? Implementation challenges for software-defined networks

Cloud services are exploding, and organizations are converging their data centers in order to take advantage of the predictability, continuity, and quality of service delivered by virtualization technologies. In parallel, energy-efficient and high-security networking is of increasing importance. Network operators, and service and product providers require a new network solution to efficiently tackle the increasing demands of this changing network landscape. Software-defined networking has emerged as an efficient network technology capable of supporting the dynamic nature of future network functions and intelligent applications while lowering operating costs through simplified hardware, software, and management. In this article, the question of how to achieve a successful carrier grade network with software-defined networking is raised. Specific focus is placed on the challenges of network performance, scalability, security, and interoperability with the proposal of potential solution directions.

Architecture & Planning• 2013• Institute of Electrical and Electronics Engineers
Access Full TextView Details
Periodicals

Explainable Machine Learning for Scientific Insights and Discoveries

Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data. A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. In this article, we review explainable machine learning in view of applications in the natural sciences and discuss three core elements that we identified as relevant in this context: transparency, interpretability, and explainability. With respect to these core elements, we provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
Access Full TextView Details
Periodicals

Industry 4.0 Concept: Background and Overview

&lt;p class="0abstract"&gt;Industry 4.0 is a strategic initiative recently introduced by the German government. The goal of the initiative is transformation of industrial manufacturing through digitalization and exploitation of potentials of new technologies. An Industry 4.0 production system is thus flexible and enables individualized and customized products. The aim of this paper is to present and facilitate an understanding of Industry 4.0 concepts, its drivers, enablers, goals and limitations. Building blocks are described and smart factory concept is presented. A Reference Architecture Model RAMI4.0 and role of standardization in future implementation of Industry 4.0 concept are addressed. The current status of Industry 4.0 readiness of the German companies is presented and commented. Finally it is discussed if Industry 4.0 is really a disruptive concept or simply a natural incremental development of industrial production systems.&lt;/p&gt;

Architecture & Planning• 2017• kassel university press
Access Full TextView Details
Periodicals

<scp>psrchive</scp> and <scp>psrfits</scp>: An Open Approach to Radio Pulsar Data Storage and Analysis

Abstract A new set of software applications and libraries for use in the archival and analysis of pulsar astronomical data is introduced. Known collectively as the psrchive scheme, the code was developed in parallel with a new data storage format called psrfits , which is based on the Flexible Image Transport System (FITS). Both of these projects utilise a modular, object-oriented design philosophy. psrchive is an open source development environment that incorporates an extensive range of c ++ object classes and pre-built command line and graphical utilities. These deal transparently and simultaneously with multiple data storage formats, thereby enhancing data portability and facilitating the adoption of the psrfits file format. Here, data are stored in a series of modular header–data units that provide flexibility and scope for future expansion. As it is based on FITS, various standard libraries and applications may be used for data input, output, and visualisation. Both psrchive and psrfits are made publicly available to the academic community in the hope that this will promote their widespread use and acceptance.

Architecture & Planning• 2004• Cambridge University Press
Access Full TextView Details
Periodicals

Exploiting the 60 GHz band for local wireless multimedia access: prospects and future directions

This article addresses basic issues regarding the design and development of wireless access and wireless LAN systems that will operate in the 60 GHz band as part of the fourth-generation (4G) system. The 60 GHz band is of much interest since this is the band in which a massive amount of spectral space (5 GHz) has been allocated worldwide for dense wireless local communications. The article gives an overview of 60 GHz channel characteristics and puts them in their true perspective. In addition, we discuss how to achieve the exploitation of the abundant bandwidth resource for all kinds of short-range communications. The main tenor is that an overall system architecture should be worked out that provides industry with plenty of scope for product differentiation. This architecture should feature affordability, scalability, modularity, extendibility, and interoperability. In addition, user convenience and easy and efficient network deployment are important prerequisites for market success. This article discusses these features and indicates a number of key research topics.

Architecture & Planning• 2002• Institute of Electrical and Electronics Engineers
Access Full TextView Details
Periodicals

MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites

Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites. Here, we introduce the MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier. Our tool can be run both locally and as a free online service via the OpenNeuro.org portal. The classifier is trained on a publicly available, multi-site dataset (17 sites, N = 1102). We perform model selection evaluating different normalization and feature exclusion approaches aimed at maximizing across-site generalization and estimate an accuracy of 76%±13% on new sites, using leave-one-site-out cross-validation. We confirm that result on a held-out dataset (2 sites, N = 265) also obtaining a 76% accuracy. Even though the performance of the trained classifier is statistically above chance, we show that it is susceptible to site effects and unable to account for artifacts specific to new sites. MRIQC performs with high accuracy in intra-site prediction, but performance on unseen sites leaves space for improvement which might require more labeled data and new approaches to the between-site variability. Overcoming these limitations is crucial for a more objective quality assessment of neuroimaging data, and to enable the analysis of extremely large and multi-site samples.

Architecture & Planning• 2017• Public Library of Science
Access Full TextView Details
Previous1...4142434445...52Next