Complex-valued weights are used in the first layer of a feed forward neural network to produce a `transform' neural network. This network was applied to a phase-uncertain sine wave detection task against a Gaussian white noise background. When compared with results of a human observer study on this task by Burgess et al., performance of the transform network was found to be nearly equal to that of an ideal observer and far superior to that of the human observers. Performance was found to be dramatically affected by initial values of the weights, which is explained in terms of concepts from statistical decision theory.
Neural networks are applied to a Rayleigh discrimination task for simulated limited-view computed tomography with maximum-entropy reconstruction. Network performance is compared to that obtained using the best machine approximation to the ideal observer found in an earlier investigation. Results obtained on 2D subimage inputs are compared with those for 1D inputs and presented previously at this conference. Back-propagation neural networks significantly outperform the `best' standard nonadaptive linear machine observer and also the intuitively appealing `matched filter' obtained by averaging over the images in a large training data set. In addition, the back-propagation neural network operating on 2D subimages performs significantly better than that limited to 1D inputs. Finally, improved performance on this Rayleigh task is found for nonlinear (over linear, that is, simple perceptron) neural network decision strategies.
Neural networks are applied to the Rayleigh discrimination task. Network performance is compared to results obtained previously using human viewers, and to the best machine approximation to the ideal observer found in an earlier investigation. We find that simple preprocessing of the input image, in this case by projection, greatly improves network convergence and only results obtained on projections are presented here. It is shown that back propagation neural networks significantly outperform a standard nonadaptive linear machine also operating on the projections. In addition, this back propagation neural network performs competitively to a nonadaptive machine that uses the complete two-dimensional information, even though some relevant information is destroyed in the projection process. Finally, improved performance on this Rayleigh task is found for nonlinear (over linear) neural network decision strategies.
Neural networks were applied to the task of detecting simulated low contrast lesions in limited-view reconstruction tomography images. Results were compared with those for theoretically derived machine observers and for human observers. Preliminary results indicated improved neural network performance for the small data set on which human observer data had been obtained, but further results for a larger data set give performance generally inferior to the best machine observer.
In many areas of practical interest, for example medical decision making problems, input data for training and testing neural networks are severely limited in number, are corrupted by noise, and may be highly correlated. In this study we examine these factors by investigating network performance on a simulated Gaussian data set with known first and second order statistics. Following the work of Wagner et al. for statistical (likelihood- ratio) classifiers, we study how the addition of noisy/correlated features affects the performance of neural network classifiers. Results are similar to that of the previous study, demonstrating that for small data sets, additional noisy/correlated features in fact degrade network performance. In addition, the use of sophisticated statistical techniques including the jackknife, Fukunaga-Hayes group jackknife, and bootstrap to estimate performance variation and remove small-sample bias are examined and found to offer significant advantages.
Neural network based analysis of ultrasound image data was carried out on liver scans of normal subjects and those diagnosed with diffuse liver disease. In a previous study, ultrasound images from a group of normal volunteers, Gaucher's disease patients, and hepatitis patients were obtained by Garra et al., who used classical statistical methods to distinguish from among these three classes. In the present work, neural network classifiers were employed with the same image features found useful in the previous study for this task. Both standard backpropagation neural networks and a recently developed biologically-inspired network called Dystal were used. Classification performance as measured by the area under a receiver operating characteristic curve was generally excellent for the back propagation networks and was roughly comparable to that of classical statistical discriminators tested on the same data set and documented in the earlier study. Performance of the Dystal network was significantly inferior; however, this may be due to the choice of network parameter. Potential methods for enhancing network performance was identified.
A back propagation neural network was used to compress simulated nuclear medicine liver images with and without simulated lesions. The network operated on the Gabor representation of the image, in order to take advantage of the apparent similarity between that representation and the natural image processing of the human visual system. The quality of the compression scheme was assessed objectively by comparing the original images to the compressed/reconstructed images through calculation of an index shown to track with human observers for this class of image, the Hotelling trace. Task performance was measured pre- and post-compression for the task of classifying normal versus abnormal livers. Compression of even 2:1 was found to result in significant performance degradation in comparison with other means of compression, but produced a visually pleasing image.
Compression methods based on Gabor functions are implemented for simulated nuclear medicine liver images with and without simulated lesions. The quality of the compression schemes are assessed objectively by comparing the original images to the compressed images through calculation of the Hotelling trace, an index shown to track with human observers for images from this modality.1 For compression based on thresholding the complex Gabor coefficients, better than 2: 1 compression is obtained without significant degradation in image quality.
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