Tagged: Application software

Training neural networks for computer-aided diagnosis: experience in the intelligence community

Neural networks are often used in computer-aided diagnosis (CAD) systems for detecting clinically significant objects. They have also been applied in the AI community to cue image analysts (IAs) for assisted target recognition and wide-area searching. Given the similarity between the applications in the two communities, there are a number of common issues that must be considered when training these neural networks. Two such issues are: (1) exploiting information at multiple scales (e.g. context and detail structure), and (2) dealing with uncertainty (e.g. errors in truth data). We address these two issues, transferring architectures and training algorithms originally developed for assisting IAs in search applications, to improve CAD for mammography. These include hierarchical pyramid neural net (HPNN) architectures that automatically learn and integrate multi-resolution features for improving microcalcification and mass detection in CAD systems. These networks are trained using an uncertain object position (UOP) error function for the supervised learning of image searching/detection tasks when the position of the objects to be found is uncertain or ill-defined. The results show that the HPNN architecture trained using the UOP error function reduces the false-positive rate of a mammographic CAD system by 30%-50% without any significant loss in sensitivity. We conclude that the transfer of assisted target recognition technology from the AI community to the medical community can significantly impact the clinical utility of CAD systems.

Using single-trial EEG to estimate the timing of target onset during rapid serial visual presentation

The timing of a behavioral response, such as a button press in reaction to a visual stimulus, is highly variable across trials. In this paper we describe a methodology for single-trial analysis of electroencephalography (EEG) which can be used to reduce the error in the estimation of the timing of the behavioral response and thus reduce the error in estimating the onset time of the stimulus. We consider a rapid serial visual presentation (RSVP) paradigm consisting of concatenated video clips and where subjects are instructed to respond when they see a predefined target. We show that a linear discriminator, with inputs distributed across sensors and time and chosen via an information theoretic feature selection criterion, can be used in conjunction with the response to yield a lower error estimate of the onset time of the target stimulus compared to the response time. We compare our results to response time and previous EEG approaches using fixed windows in time, showing that our method has the lowest estimation error. We discuss potential applications, specifically with respect to cortically-coupled computer vision based triage of large image databases

Hierarchical multi-resolution models for object recognition: Applications to mammographic computer-aided diagnosis

A fundamental problem in image analysis is the integration of information across scale to detect and classify objects. We have developed, within a machine learning framework, two classes of multiresolution models for integrating scale information for object detection and classification-a discriminative model called the hierarchical pyramid neural network and a generative model called a hierarchical image probability model. Using receiver operating characteristic analysis, we show that these models can significantly reduce the false positive rates for a well-established computer-aided diagnosis system.