Christopher R. Dance

Automatic processing of document annotations

A common authoring technique involves making annotations on a printed draft and then typing the corrections into a computer at a later date. In this paper, we describe a system that goes some way towards automating this process. The author simply passes the annotated documents through a sheetfeed scanner and then brings up the electronic document in a text editor. The system then works out where the annotated words are and allows the author to skip from one annotation to the next at the touch of a key. At the heart of the system lies a procedure for reliably establishing correspondences between printed words and their electronic counterparts, without performing optical character recognition. This procedure might have interesting applications in document database retrieval, since it allows an electronic document to be indexed by a printed version of itself.

Feature selection using expected attainable discrimination

We propose expected attainable discrimination (EAD) as a measure to select discrete valued features for reliable discrimination between two classes of data. EAD is an average of the area under the ROC curves obtained when a simple histogram probability density model is trained and tested on many random partitions of a data set. EAD can be incorporated into various stepwise search methods to determine promising subsets of features, particularly when misclassification costs are difficult or impossible to specify. Experimental application to the problem of risk prediction in pregnancy is described.

Interactive Segmentation of 3D Ultrasound Using Deformable Solid Models and Active Contours

This paper presents a prototype segmentation system for three-dimensional ultrasound data. 3D ultrasound is cheap and non-nvasive but the data has a low signal-to-noise ratio and contains artifacts. To overcome these difficulties we have developed a system which uses a prior model, initialised by a clinician, to provide the starting point for a data-driven segmentation algorithm based on active contours. Results are presented showing how the technique can facilitate the segmentation of a gall-bladder.

What patient information allows us to make accurate predictions of outcome

Only some of the information contained in a medical record will be useful to the prediction of patient outcome. The authors describe a novel method for selecting those outcome predictors which allow them to reliably discriminate between adverse and benign end results. Using the area under the receiver operating characteristic as a nonparametric measure of discrimination, the authors show how to calculate the maximum discrimination attainable with a given set of discrete valued features. This upper limit forms the basis of their feature selection algorithm. They use the algorithm to select features (from maternity records) relevant to the prediction of failure to progress in labour. The results of this analysis motivate investigation of those predictors of failure to progress relevant to parous and nulliparous-sub-populations.