Siemens Medical Solutions Usa, Inc.

Marvellworld Trade Ltd.

Vikas C Raykar

R Bharat Rao

Igor V Kozintsev

Marcos Salganicoff

Matthias Wolf

Balaji Krishnapuram

Meizhu Liu

Le Lu

Murat Dundar

Rainer Lienhart

Described herein is a framework for multi-view matching of regions of interest in images. According to one aspect, a processor receives first and second digitized images, as well as at least one CAD finding corresponding to a detected region of interest in the first image. The processor determines at least one candidate location in the second image that matches the CAD finding in the first image. The matching is performed based on local appearance features extracted for the CAD finding and the candidate location. In accordance with another aspect, the processor receives digitized training images representative of at least first and second views of one or more regions of interest. Feature selection is performed based on the training images to select a subset of relevant local appearance features to represent instances in the first and second views. A distance metric is then learned based on the subset of local appearance features. The distance metric may be used to perform matching of the regions of interest.

Matching of regions of interest across multiple views

A first computing device transmitting a wireless signal to a second and third computing devices, the signal requesting an actuator of the second computing device generate an acoustic signal to be received by a sensor of the third computing device, wherein the actuator and sensor are unsynchronized. The first computing device computes, based on an time estimate for the acoustic signal to travel from the actuator of the second computing device to the sensor of the third computing device, a physical location of the actuator of the second computing device and the sensor of the third computing device.

Three-dimensional position calibration of audio sensors and actuators on a distributed computing platform

A method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of images, each image including one or more candidate regions that have been identified as suspicious by a computer aided diagnosis system. Each image has been manually annotated to identify malignant regions. Multiple instance learning is applied to train a classifier to classify suspicious regions in a new image as malignant or benign by identifying those candidate regions that overlap a same identified malignant region, grouping each candidate region that overlaps the same identified malignant region into a same bag, and maximizing a probability wherein N is a number of bags, pis a probability of bag i containing a candidate region that overlaps with an identified malignant region, and yis a label where a value of 1 indicates malignancy and 0 otherwise.

System and method for multiple-instance learning for computer aided diagnosis

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Conshohocken, PA, United States of America

Vikas C Raykar