5,936 research outputs found
Intelligent computing applications to assist perceptual training in medical imaging
The research presented in this thesis represents a body of work which addresses issues in medical imaging, primarily as it applies to breast cancer screening and laparoscopic surgery. The concern here is how computer based methods can aid medical practitioners in these tasks. Thus, research is presented which develops both new techniques of analysing radiologists performance data and also new approaches of examining surgeons visual behaviour when they are undertaking laparoscopic training.
Initially a new chest X-Ray self-assessment application is described which has been developed to assess and improve radiologists performance in detecting lung cancer. Then, in breast cancer screening, a method of identifying potential poor performance outliers at an early stage in a national self-assessment scheme is demonstrated. Additionally, a method is presented to optimize whether a radiologist, in using this scheme, has correctly localised and identified an abnormality or made an error.
One issue in appropriately measuring radiological performance in breast screening is that both the size of clinical monitors used and the difficulty in linking the medical image to the observer s line of sight hinders suitable eye tracking. Consequently, a new method is presented which links these two items.
Laparoscopic surgeons have similar issues to radiologists in interpreting a medical display but with the added complications of hand-eye co-ordination. Work is presented which examines whether visual search feedback of surgeons operations can be useful training aids
Lesion detection in epilepsy surgery: Lessons from a prospective evaluation of a machine learning algorithm
AIM: To evaluate a lesion detection algorithm designed to detect focal cortical dysplasia (FCD) in children undergoing stereoelectroencephalography (SEEG) as part of their presurgical evaluation for drug-resistant epilepsy. METHOD: This was a prospective, single-arm, interventional study (Idea, Development, Exploration, Assessment, and Long-Term Follow-Up phase 1/2a). After routine SEEG planning, structural magnetic resonance imaging sequences were run through an FCD lesion detection algorithm to identify putative clusters. If the top three clusters were not already sampled, up to three additional SEEG electrodes were added. The primary outcome measure was the proportion of patients who had additional electrode contacts in the SEEG-defined seizure-onset zone (SOZ). RESULTS: Twenty patients (median age 12 years, range 4-18 years) were enrolled, one of whom did not undergo SEEG. Additional electrode contacts were part of the SOZ in 1 out of 19 patients while 3 out of 19 patients had clusters that were part of the SOZ but they were already implanted. A total of 16 additional electrodes were implanted in nine patients and there were no adverse events from the additional electrodes. INTERPRETATION: We demonstrate early-stage prospective clinical validation of a machine learning lesion detection algorithm used to aid the identification of the SOZ in children undergoing SEEG. We share key lessons learnt from this evaluation and emphasize the importance of robust prospective evaluation before routine clinical adoption of such algorithms
Subspace discovery for video anomaly detection
PhDIn automated video surveillance anomaly detection is a challenging task. We address
this task as a novelty detection problem where pattern description is limited
and labelling information is available only for a small sample of normal instances.
Classification under these conditions is prone to over-fitting. The contribution of this
work is to propose a novel video abnormality detection method that does not need
object detection and tracking. The method is based on subspace learning to discover
a subspace where abnormality detection is easier to perform, without the need of
detailed annotation and description of these patterns. The problem is formulated as
one-class classification utilising a low dimensional subspace, where a novelty classifier
is used to learn normal actions automatically and then to detect abnormal actions
from low-level features extracted from a region of interest. The subspace is discovered
(using both labelled and unlabelled data) by a locality preserving graph-based algorithm
that utilises the Graph Laplacian of a specially designed parameter-less nearest
neighbour graph.
The methodology compares favourably with alternative subspace learning algorithms
(both linear and non-linear) and direct one-class classification schemes commonly
used for off-line abnormality detection in synthetic and real data. Based on
these findings, the framework is extended to on-line abnormality detection in video
sequences, utilising multiple independent detectors deployed over the image frame to
learn the local normal patterns and infer abnormality for the complete scene. The
method is compared with an alternative linear method to establish advantages and
limitations in on-line abnormality detection scenarios. Analysis shows that the alternative
approach is better suited for cases where the subspace learning is restricted on
the labelled samples, while in the presence of additional unlabelled data the proposed
approach using graph-based subspace learning is more appropriate
Examining the clinical utility of the modified Alarm Distress Baby Scale (m-ADBB) for the detection of early signs of Autism Spectrum Disorder
Background / Aim Signs of Autism Spectrum Disorder (ASD) can be observed in the first two years of life. However early detection of ASD remains challenging, partly because no reliable and easy to use routine screening instrument for infants and toddlers is currently available to clinicians. Some early ASD related deficits in social communication and interaction are also typical of sustained social withdrawal in infants and toddlers. The current study aims to test whether a brief observational screening instrument for social withdrawal in infants, the modified Alarm Distress Baby Scale (m-ADBB), may be clinically useful for detection of ASD in the first two years of life. It is hypothesised, that children with ASD will score higher on the m-ADBB than typically developing (TD) children, indicating more symptoms of social withdrawal. Method Home-video recordings of children with ASD and of TD children from approximately age 12 month and 24 month were analysed using the m-ADBB. Results Home-videos of 10 children with ASD and 10 TD children were available at each age. Children with a diagnosis of ASD scored statistically significantly higher on the m-ADBB than TD children at 12 month (Z=-2.54; p=0.023; r=-0.57) and at 24 month (Z=-2.40; p=0.023; r=-0.54). Five of ten children with ASD met the m-ADBB criterion for social withdrawal in their 12 month videos, and four out of ten in their 24 month videos. Using a lower cut-off score increased detection rates (7 at 12 month; 8 at 24 month). False positive rates were low at both ages and for both cut-off scores (range 1 to 3 out of 10). Conclusion Observing only five social withdrawal behaviours as operationalized by the m-ADBB appears to be useful in flagging possible presence of ASD during the first two years of life. The scale’s sensitivity and specificity for ASD detection needs to be established in a larger sample
Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models
In this paper, we present an unsupervised learning framework for analyzing
activities and interactions in surveillance videos. In our framework, three
levels of video events are connected by Hierarchical Dirichlet Process (HDP)
model: low-level visual features, simple atomic activities, and multi-agent
interactions. Atomic activities are represented as distribution of low-level
features, while complicated interactions are represented as distribution of
atomic activities. This learning process is unsupervised. Given a training
video sequence, low-level visual features are extracted based on optic flow and
then clustered into different atomic activities and video clips are clustered
into different interactions. The HDP model automatically decide the number of
clusters, i.e. the categories of atomic activities and interactions. Based on
the learned atomic activities and interactions, a training dataset is generated
to train the Gaussian Process (GP) classifier. Then the trained GP models work
in newly captured video to classify interactions and detect abnormal events in
real time. Furthermore, the temporal dependencies between video events learned
by HDP-Hidden Markov Models (HMM) are effectively integrated into GP classifier
to enhance the accuracy of the classification in newly captured videos. Our
framework couples the benefits of the generative model (HDP) with the
discriminant model (GP). We provide detailed experiments showing that our
framework enjoys favorable performance in video event classification in
real-time in a crowded traffic scene
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