Tagged: Target recognition

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.

Spatial signatures of visual object recognition events learned from single-trial analysis of EEG

In this paper we use linear discrimination for learning EEG signatures of object recognition events in a rapid serial visual presentation (RSVP) task. We record EEG using a high spatial density array (63 electrodes) during the rapid presentation (50-200 msec per image) of natural images. Each trial consists of 100 images, with a 50% chance of a single target being in a trial. Subjects are instructed to press a left mouse button at the end of the trial if they detected a target image, otherwise they are instructed to press the right button. Subject EEG was analyzed on a single-trial basis with an optimal spatial linear discriminator learned at multiple time windows after the presentation of an image. Analysis of discrimination results indicated a periodic fluctuation (time-localized oscillation) in A/sub z/ performance. Analysis of the EEG using the discrimination components learned at the peaks of the A/sub z/ fluctuations indicate 1) the presence of a positive evoked response, followed in time by a negative evoked response in strongly overlapping areas and 2) a component which is not correlated with the discriminator learned during the time-localized fluctuation. Results suggest that multiple signatures, varying over time, may exist for discriminating between target and distractor trials.

Hierarchical image probability (HIP) models

We formulate a model for probability distributions on image spaces. We show that any distribution of images can be factored exactly into conditional distributions of feature vectors at one resolution (pyramid level) conditioned on the image information at lower resolutions. We would like to factor this over positions in the pyramid levels to make it tractable, but such factoring may miss long-range dependencies. To capture long-range dependencies, we introduce hidden class labels at each pixel in the pyramid. The result is a hierarchical mixture of conditional probabilities, similar to a hidden Markov model on a tree. The model parameters can be found with maximum likelihood estimation using the EM algorithm. We have obtained encouraging preliminary results on the problems of detecting various objects in SAR images and target recognition in optical aerial images.