7,784 research outputs found
Introducing alternative-based thresholding for defining functional regions of interest in fMRI
In fMRI research, one often aims to examine activation in specific functional regions of interest (fROIs). Current statistical methods tend to localize fROIs inconsistently, focusing on avoiding detection of false activation. Not missing true activation is however equally important in this context. In this study, we explored the potential of an alternative-based thresholding (ABT) procedure, where evidence against the null hypothesis of no effect and evidence against a prespecified alternative hypothesis is measured to control both false positives and false negatives directly. The procedure was validated in the context of localizer tasks on simulated brain images and using a real data set of 100 runs per subject. Voxels categorized as active with ABT can be confidently included in the definition of the fROI, while inactive voxels can be confidently excluded. Additionally, the ABT method complements classic null hypothesis significance testing with valuable information by making a distinction between voxels that show evidence against both the null and alternative and voxels for which the alternative hypothesis cannot be rejected despite lack of evidence against the null
Active acquisition for multimodal neuroimaging
In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field-of-view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery
Detection and classification of neurodegenerative diseases: a spatially informed bayesian deep learning approach
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesNeurodegenerative diseases comprise a group of chronic and irreversible conditions
characterized by the progressive degeneration of the structure and function
of the central nervous system. The detection and classification of patients according
to the underlying disease are crucial for developing oriented treatments and
enriching prognosis. In this context, Magnetic resonance imaging (MRI) data can
provide meaningful insights into neurodegeneration by detecting the physiological
manifestations in the brain caused by the disease processes. One field of extensive
clinical use of MRI is the accurate and automated classification of neurodegenerative
disorders. Most studies distinguish patients from healthy subjects or stages
within the same disease. Such distinction does not mirror clinical practice, as a
patient may not show all symptoms, especially if the disease is in an early stage,
or show, due to comorbidities, other symptoms as well. Likewise, automated
classifiers are partly suited for medical diagnosis since they cannot produce probabilistic
predictions nor account for uncertainty. Also, existent studies ignore the
spatial heterogeneity of the brain alterations caused by neurodegenerative processes.
The spatial configuration of the neuronal loss is a characteristic hallmark
for each disorder. To fill these gaps, this thesis aims to develop a classification
technique that incorporates uncertainty and spatial information for distinguishing
four neurodegenerative diseases, Alzheimer’s disease, Mild cognitive impairment,
Parkinson’s disease and Multiple Sclerosis, and healthy subjects. This technique
will produce automated, contingent, and accurate predictions to support clinical
diagnosis.
To quantify prediction uncertainty and improve classification accuracy, this study
introduces a Bayesian neural network with a spatially informed input. A convolutional
neural network (CNN) is developed to identify a neurodegenerative
condition based on T1weighted MRI scans from patients and healthy controls.
Bayesian inference is incorporated into the CNN to measure uncertainty and produce
probabilistic predictions. Also, a spatially informed MRI scan is added to
the CNN to improve feature detection and classification accuracy.
The Spatially informed Bayesian Neural Network (SBNN) proposed in this work
demonstrates that classification accuracy can be increased up to 25% by including
the spatially informed MRI scan. Furthermore, the SBNN provides robust
probabilistic diagnosis that resembles clinical decision-making and accounts for
atypical, numerous, and early presentations of neurodegenerative disorders
Scan matching by cross-correlation and differential evolution
Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85
Automatically combining static malware detection techniques
Malware detection techniques come in many different flavors, and cover different effectiveness and efficiency trade-offs. This paper evaluates a number of machine learning techniques to combine multiple static Android malware detection techniques using automatically constructed decision trees. We identify the best methods to construct the trees. We demonstrate that those trees classify sample apps better and faster than individual techniques alone
Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities
The analysis of ultrasonic NDE data has traditionally been addressed by a
trained operator manually interpreting data with the support of rudimentary
automation tools. Recently, many demonstrations of deep learning (DL)
techniques that address individual NDE tasks (data pre-processing, defect
detection, defect characterisation, and property measurement) have started to
emerge in the research community. These methods have the potential to offer
high flexibility, efficiency, and accuracy subject to the availability of
sufficient training data. Moreover, they enable the automation of complex
processes that span one or more NDE steps (e.g. detection, characterisation,
and sizing). There is, however, a lack of consensus on the direction and
requirements that these new methods should follow. These elements are critical
to help achieve automation of ultrasonic NDE driven by artificial intelligence
such that the research community, industry, and regulatory bodies embrace it.
This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by
DL methodologies. The review is organised by the NDE tasks that are addressed
by means of DL approaches. Key remaining challenges for each task are noted.
Basic axiomatic principles for DL methods in NDE are identified based on the
literature review, relevant international regulations, and current industrial
needs. By placing DL methods in the context of general NDE automation levels,
this paper aims to provide a roadmap for future research and development in the
area.Comment: Accepted version to be published in NDT & E Internationa
- …