Arizona State University

Mayo Foundation for Medical Education and Research

Arizona Board of Regents, a Body Corporate

Wenzhe Xue

Jianming Liang

Nima Tajbakhsh

Hong Wu

Marek Belohlavek

Eileen M McMahon

Receiving a plurality of images of a first wall of the patient's heart, the plurality including a first image captured at a time ij and a second image captured at a time t, determining, based on the plurality of images, a first characteristic of movement of at least a wall portion of the first wall during a time period spanning time tand time t, the first characteristic of movement being determined by using one or more image processing techniques, and the first, characteristic of movement being based on at least one of: (i) a shape of the wall portion in the first image and a shape of the wall portion in the second image, and (ii) a location of the wall portion in the first image and a location of the wall portion in the second image: and outputting an indication of the first characteristic of movement.

Systems, methods, and media for monitoring the condition of a patient's heart

Systems, methods, and media for on-line boosting of a classifier are provided, comprising: receiving a training sample; for each of a plurality of features, determining a feature value for the training sample and the feature, using the feature value to update a histogram, and determining a threshold for a classifier of the feature; for each of the plurality of features, classifying the training sample using the threshold for the classifier of the feature and calculating an error associated with the classifier; selecting a plurality of best classifiers from the classifiers; and, for each of the plurality of best classifiers, assigning a voting weight to the one of the plurality of best classifiers.

Systems, methods, and media for on-line boosting of a classifier

Systems, methods, and media for updating a classifier are provided, in some embodiments, systems for updating a classifier are provided, the systems comprising: a hardware processor that is configured to: receive a sample; for each of a first plurality of weak learners, classify the sample using the weak learner, determine an outcome of the classification, and determine an up-dated error rate of the weak learner based on the outcome of the classification and at least one of: (i) a count of positive samples used to update the classifier, and (ii) a count of negative samples used to update the classifier; select a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and update tire classifier based on the first weak learner.

Systems, methods, and media for updating a classifier

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Chandler, AZ, United States of America

Wenzhe Xue