Abstract: A method for computer aided detection of anatomical abnormalities in medical images includes providing a plurality of abnormality candidates and features of said abnormality candidates, and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers of the form sign(wTx+b), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more complex features are used for each successive stage of said cascade of classifiers.

Abstract: A method for finding a ranking function ƒ that classifies feature points in an n-dimensional space includes providing a plurality of feature points xk derived from tissue sample regions in a digital medical image, providing training data A comprising training samples Aj where A = ? j = 1 S ? ( A j = { x i j } i = 1 m j ) , providing an ordering E={(P,Q)|APAQ} of at least some training data sets where all training samples xi?AP are ranked higher than any sample xj?AQ, solving a mathematical optimization program to determine the ranking function ƒ that classifies said feature points x into sets A. For any two sets Ai, Aj, AiAj, and the ranking function ƒ satisfies inequality constraints ƒ(xi)?ƒ(xj) for all xi?conv(Ai) and xj?conv(Aj), where conv(A) represents the convex hull of the elements of set A.

Abstract: A cardiac view of a medical ultrasound image is automatically identified. By grouping different views into sub-categories, a hierarchal classifier identifies the views. For example, apical views are distinguished from parasternal views. Specific types of apical or parasternal views are identified based on distinguishing between images of the geneses. Different features are used for classifying, such as gradients, functions of the gradients, statistics of an average frame of data from a clip or sequence of frames, or a number of edges along a given direction. The number of features used may be compressed, such as by classifying a plurality of features into a new feature. For example, alpha weights in a model of features and classes are determined and used as features for classification.

Abstract: A computer-implemented method for determining a boundary for binary classification includes providing a data set, initializing a value for noise in the data set, and determining a hyperplane dividing the data set and a slack variable given a current value for noise. The method further includes updating the value for noise and the slack variable given the hyperplane, and determining the hyperplane to be the boundary for binary classification of the data set upon determining a termination criterion to be met, wherein elements of the data set are classified according to the boundary.

Abstract: Hierarchal modeling is used to distinguish one state or class from three or more classes. In a first stage, a normal or other class is distinguished from a diseased or other groups of classes. If the results of the first stage classification indicate diseased or data within the groups of different classes, a subsequent stage of classification is performed. In a subsequent stage of classification, the data is classified to distinguish one or more other classes from the remaining classes. Using two or more stages, medical information is classified by eliminating one or more possible classes in each stage to finally identify a particular class most appropriate or probable for the data.

Abstract: A method and device having instructions for analyzing input data-space by learning classifiers include choosing a candidate subset from a predetermined training data-set that is used to analyze the input data-space. Candidates are temporarily added from the candidate subset to an expansion set to generate a new kernel space for the input data-space by predetermined repeated evaluations of leave-one-out errors for the candidates added to the expansion set. This is followed by removing the candidates temporarily added to the expansion set after the leave-one-out error evaluations are performed, and selecting the candidates to be permanently added to the expansion set based on the leave-one-out errors of the candidates temporarily added to the expansion set to determine the one or more classifiers.

Abstract: A method and device with instructions for analyzing an image data-space includes creating a library of one or more kernels, wherein each kernel from the library of the kernels maps the image data-space to a first data-space using at least one mapping function; and learning a linear combination of kernels in an automatic manner to generate at least one of a classifier and a regressor which is applied to the first data-space. The linear combination of kernels is used to generate a classified image-data space to detect at least one of the candidates in the classified image-data space.