Raykar
Vikas Raykar, Bangalore IN
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20150370887 | SEMANTIC MERGE OF ARGUMENTS - A method comprising using at least one hardware processor for: receiving a topic under consideration (TUC) and a set of claims referring to the TUC; identifying semantic similarity relations between claims of the set of claims; clustering the claims into a plurality of claim clusters based on the identified semantic similarity relations, wherein said claim clusters represent semantically different claims of the set of claims; and generating a list of non-redundant claims comprising said semantically different claims. | 12-24-2015 |
20150371651 | AUTOMATIC CONSTRUCTION OF A SPEECH - A method comprising using at least one hardware processor for: identifying relations between pairs of claims of a set of claims; aggregating the claims of the set of claims into a plurality of clusters based on the identified relations; generating a plurality of arguments from the plurality of clusters, wherein each of the arguments is generated from a cluster of the plurality of clusters, and wherein each of the arguments comprises at least one claim of the set of claims, scoring each possible set of a predefined number of arguments of the plurality of arguments, based on a quality of each argument of the predefined number of arguments and on diversity between the predefined number of arguments; and generating a speech, wherein the speech comprises a top scoring possible set of the possible set of the predefined number of arguments. | 12-24-2015 |
Vikas C. Raykar, Conshohocken, PA US
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20090080731 | System and Method for Multiple-Instance Learning for Computer Aided Diagnosis - 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 | 03-26-2009 |
20120088981 | Matching of Regions of Interest Across Multiple Views - 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. | 04-12-2012 |
Vikas C. Raykar, Bangalore IN
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20140161337 | Adaptive Anatomical Region Prediction - Disclosed herein is a framework for facilitating adaptive anatomical region prediction. In accordance with one aspect, a set of exemplar images including annotated first landmarks is received. User definitions of first anatomical regions in the exemplar images are obtained. The framework may detect second landmarks in a subject image. It may further compute anatomical similarity scores between the subject image and the exemplar images based on the first and second landmarks, and predict a second anatomical region in the subject image by adaptively combining the first anatomical regions based on the anatomical similarity scores. | 06-12-2014 |