Showing papers by "Paul Sajda published in 1998"

Role of feature selection in building pattern recognizers for computer-aided diagnosis

Abstract: In this paper we explore the use of feature selection techniques to improve the generalization performance of pattern recognizers for computer-aided diagnosis. We apply a modified version of the sequential forward floating selection (SFFS) of Pudil et al. to the problem of selecting an optimal feature subset for mass detection in digitized mammograms. The complete feature set consists of multi-scale tangential and radial gradients in the mammogram region of interest. We train a simple multi-layer perceptron (MLP) using the SFFS algorithm and compare its performance, using a jackknife procedure, to an MLP trained on the complete feature set (35 features). Results indicate that a variable number of features is chosen in each of the jackknife sets (12 +/- 4) and the test performance, Az, using the chosen feature subset is no better than the performance using the entire feature set. These results may be attributed to the fact that the feature set is noisy and the data set used for training/testing is small. We next modify the feature selection technique by using the results of the jackknife to compute the frequency at which different features are selected. We construct a classifier by choosing the top N features, selected most frequently, which maximize performance on the training data. We find that by adding this `hand-tuning' component to the feature selection process, we can reduce the feature set from 35 to 8 features and at the same time have a statistically significant increase in generalization performance (p < 0.015).© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Method and apparatus for training and operating a neural network for detecting breast cancer

Abstract: A method and apparatus for training and operating a neural network using gated data. The neural network is a mixture of experts that performs “soft” partitioning of a network of experts. In a specific embodiment, the technique is used to detect malignancy by analyzing skin surface potential data. In particular, the invention uses certain patient information, such as menstrual cycle information, to “gate” the expert output data into particular populations, i.e., the network is soft partitioned into the populations. An Expectation-Maximization (EM) routine is used to train the neural network using known patient information, known measured skin potential data and correct diagnosis for the particular training data and patient information. Once trained, the neural network parameters are used in a classifier for predicting breast cancer malignancy when given the patient information and skin potentials of other patients.

Applications of Multi-Resolution Neural Networks to Mammography

Abstract: We have previously presented a coarse-to-fine hierarchical pyramid/ neural network (HPNN) architecture which combines multi-scale image processing techniques with neural networks. In this paper we present applications of this general architecture to two problems in mammographic Computer-Aided Diagnosis (CAD). The first application is the detection of microcalcifications. The coarse-to-fine HPNN was designed to learn large-scale context information for detecting small objects like microcalcifications. Receiver operating characteristic (ROC) analysis suggests that the hierarchical architecture improves detection performance of a well established CAD system by roughly 50%. The second application is to detect mammographic masses directly. Since masses are large, extended objects, the coarse-to-fine HPNN architecture is not suitable for this problem. Instead we construct a fine-to-coarse HPNN architecture which is designed to learn small-scale detail structure associated with the extended objects. Our initial results applying the fine-to-coarse HPNN to mass detection are encouraging, with detection performance improvements of about 36%. We conclude that the ability of the HPNN architecture to integrate information across scales, both coarse-to-fine and fine-to-coarse, makes it well suited for detecting objects which may have contextual clues or detail structure occurring at scales other than the natural scale of the object.

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

Abstract: 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.

Dealing with position uncertainty when training an image search system

Abstract: We formulate an error function for the supervised learning of image search/detection tasks when the positions of the objects to be found are uncertain or ill-defined. The need for this uncertain object position (UOP) error function arises in at least two ways. First, point-like objects frequently have positions that are inaccurately specified. We illustrate this with the problem of detecting microcalcifications in mammograms. The second type of position uncertainty occurs with extended objects whose boundaries are not accurately defined. In this case we usually only need the detector to respond at one pixel within each object. As an example of this, we present results for neural networks trained to detect clusters of buildings in aerial photographs. We are currently applying the UOP error function to the detection of masses in mammograms, which also have poorly-defined boundaries. In all of these examples, neural networks trained with the UOP error function perform much better than networks trained with the conventional cross-entropy error function.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.