Tagged: Object detection

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.

A 3-D Immersive Environment for Characterizing EEG Signatures of Target Detection

Visual target detection is one of the most studied paradigms in human electrophysiology. Electroencephalo-graphic (EEG) correlates of target detection include the well-characterized N1, P2, and P300. In almost all cases the experimental paradigms used for studying visual target detection are extremely well-controlled – very simple stimuli are presented so as to minimize eye movements, and scenarios involve minimal active participation by the subject. However, to characterize these EEG correlates for real-world scenarios, where the target or the subject may be moving and the two may interact, a more flexible paradigm is required. The environment must be immersive and interactive, and the system must enable synchronization between events in the world, the behavior of the subject, and simultaneously recorded EEG signals. We have developed a hardware/software system that enables us to precisely control the appearance of objects in a 3D virtual environment, which subjects can navigate while the system tracks their eyes and records their EEG activity. We are using this environment to investigate a set of questions which focus on the relationship between the visibility, salience, and affect of the target; the agency and eye movements of the subject; and the resulting EEG signatures of detection. In this paper, we describe the design of our system and present some preliminary results regarding the EEG signatures of target detection.

Do We See Before We Look?

We investigated neural correlates of target detection in the electroencephalogram (EEG) during a free viewing search task and analyzed signals locked to saccadic events. Subjects performed a search task for multiple random scenes while we simultaneously recorded 64 channels of EEG and tracked subjects eye position. For each subject we identified target saccades (TS) and distractor saccades (DS). We sampled the sets of TS and DS saccades such that they were equalized/matched for saccade direction and duration, ensuring that no information in the saccade properties themselves was discriminating for their type. We aligned EEG to the saccade onset and used logistic regression (LR), in the space of the 64 electrodes, to identify activity discriminating a TS from a DS on a single-trial basis. We found significant discriminating activity in the EEG both before and after the saccade. We also saw substantial reduction in discriminating activity when the saccade was executed. We conclude that we can identify neural signatures of detection both before and after the saccade, indicating that subjects anticipate the target before the last saccade, which serves to foveate and confirm the target identity.

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.

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.