Patents by Inventor Pavel Kisilev

Pavel Kisilev has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10735141
    Abstract: A system for reducing analog noise in a noisy channel, comprising: an interface configured to receive analog channel output comprising a stream of noisy binary codewords of a linear code; and a computation component configured to perform the following: for each analog segment of the analog channel output of block length: calculating an absolute value representation and a sign representation of a respective analog segment, calculating a multiplication of a binary representation of the sign representation with a parity matrix of the linear code, inputting the absolute value representation and the outcome of the multiplication into a neural network for acquiring a neural network output, and estimating a binary codeword by component-wise multiplication of the neural network output and the sign representation.
    Type: Grant
    Filed: December 21, 2018
    Date of Patent: August 4, 2020
    Assignee: Huawei Technologies Co., Ltd.
    Inventors: Amir Bennatan, Yoni Choukroun, Pavel Kisilev, Junqiang Shen
  • Patent number: 10706575
    Abstract: Asymmetries are detected in one or more images by partitioning each image to create a set of patches. Salient patches are identified, and an independent displacement for each patch is identified. The techniques used to identify the salient patches and the displacement for each patch are combined in a function to generate a score for each patch. The scores can be used to identify possible asymmetries.
    Type: Grant
    Filed: December 6, 2017
    Date of Patent: July 7, 2020
    Assignee: International Business Machines Corporation
    Inventors: Sharon Alpert, Miri Erihov, Pavel Kisilev
  • Publication number: 20200204299
    Abstract: A system for reducing analog noise in a noisy channel, comprising: an interface configured to receive analog channel output comprising a stream of noisy binary codewords of a linear code; and a computation component configured to perform the following: for each analog segment of the analog channel output of block length: calculating an absolute value representation and a sign representation of a respective analog segment, calculating a multiplication of a binary representation of the sign representation with a parity matrix of the linear code, inputting the absolute value representation and the outcome of the multiplication into a neural network for acquiring a neural network output, and estimating a binary codeword by component-wise multiplication of the neural network output and the sign representation.
    Type: Application
    Filed: December 21, 2018
    Publication date: June 25, 2020
    Inventors: Amir BENNATAN, Yoni CHOUKROUN, Pavel KISILEV, Junqiang SHEN
  • Patent number: 10650286
    Abstract: Embodiments of the present systems and methods may provide the capability to classify medical images, such as mammograms, in an automated manner using existing annotation information. In embodiments, only the global, image level tag may be needed to classify a mammogram into certain types, without fine annotation of the findings in the image. In an embodiment, a computer-implemented method for classifying medical images may comprise receiving a plurality of image tiles, each image tile including a portion of a whole view, processed by a trained or a pre-trained model and outputting a one-dimensional feature vector for each tile to generate a three-dimensional feature volume and classifying the larger image by a trained model based on the generated three-dimensional feature volume to form a classification of the image.
    Type: Grant
    Filed: September 7, 2017
    Date of Patent: May 12, 2020
    Assignee: International Business Machines Corporation
    Inventors: Rami Ben-Ari, Pavel Kisilev, Jeremias Sulam
  • Patent number: 10546246
    Abstract: A computer-implemented method includes receiving multimodal data. The computer-implemented method further includes generating one or more kernel matrices from the multimodal data. The computer-implemented method further includes generating an equivalent kernel matrix using one or more coefficient matrices, wherein the one or more coefficient matrices are constrained by a nuclear norm. The computer-implemented method further includes initiating one or more iterative processes. Each of the one or more iterative processes includes: calculating an error for the one or more coefficient matrices of the equivalent kernel matrix based on a training set, and initiating a line search for the one or more coefficient matrices of the equivalent kernel matrix. The computer-implemented method further includes, responsive to generating an optimal coefficient matrix, terminating the one or more iterative processes. The method may be embodied in a corresponding computer system or computer program product.
    Type: Grant
    Filed: September 18, 2015
    Date of Patent: January 28, 2020
    Assignee: International Business Machines Corporation
    Inventors: Pavel Kisilev, Eli A. Meirom
  • Publication number: 20190304092
    Abstract: There is provided a method for training a deep convolutional neural network (CNN) for detecting an indication of likelihood of abnormality, comprising: receiving anatomical training images, each including an associated annotation indicative of abnormality for the whole image without an indication of location of the abnormality, executing, for each anatomical training image: decomposing the anatomical training image into patches, computing a feature representation of each patch, computing for each patch, according to the feature representation of the patch, a probability that the patch includes an indication of abnormality, setting a probability indicative of likelihood of abnormality in the anatomical image according to the maximal probability value computed for one patch, and training a deep CNN for detecting an indication of likelihood of abnormality in a target anatomical image according to the patches of the anatomical training images, the one patch, and the probability set for each respective anatomical
    Type: Application
    Filed: March 28, 2018
    Publication date: October 3, 2019
    Inventors: Ayelet Akselrod-Ballin, Ran Bakalo, Rami Ben-Ari, Yoni Choukroun, Pavel Kisilev
  • Patent number: 10373073
    Abstract: A computer implemented method of automatically creating a classification function trained with augmented representation of features extracted from a plurality of sample media objects using one or more hardware processors for executing a code. The code comprises code instructions for extracting a plurality of features from a plurality of sample media objects, generating a plurality of feature samples for each of the plurality of features by augmenting the plurality of features, training a classification function with the plurality of features samples and outputting the classification function for classifying one or more new media objects.
    Type: Grant
    Filed: January 11, 2016
    Date of Patent: August 6, 2019
    Assignee: International Business Machines Corporation
    Inventor: Pavel Kisilev
  • Patent number: 10282059
    Abstract: In one implementation, a plurality of signature vectors from a multi-dimensional representation of a graphical object is generated. Each of the signature vectors comprises attributes that vary little in response to changes in shape, size, orientation, and visual layer appearance of the graphical object, and each of the signature vectors includes attributes based on operations of integration, differentiation, and transforms on the multi-dimensional representation of the graphical object. Each signature vector is composited into multiple portions from the plurality of signature vectors to define an appearance-invariant signature of the graphical object.
    Type: Grant
    Filed: October 23, 2015
    Date of Patent: May 7, 2019
    Assignee: ENTIT SOFTWARE LLC
    Inventors: Daniel Freedman, Pavel Kisilev, Anastasia Dubrovina, Sagi Schein, Ruth Bergman
  • Publication number: 20190073569
    Abstract: Embodiments of the present systems and methods may provide the capability to classify medical images, such as mammograms, in an automated manner using existing annotation information. In embodiments, only the global, image level tag may be needed to classify a mammogram into certain types, without fine annotation of the findings in the image. In an embodiment, a computer-implemented method for classifying medical images may comprise receiving a plurality of image tiles, each image tile including a portion of a whole view, processed by a trained or a pre-trained model and outputting a one-dimensional feature vector for each tile to generate a three-dimensional feature volume and classifying the larger image by a trained model based on the generated three-dimensional feature volume to form a classification of the image.
    Type: Application
    Filed: September 7, 2017
    Publication date: March 7, 2019
    Inventors: Rami Ben-Ari, Pavel Kisilev, Jeremias Sulam
  • Patent number: 10140709
    Abstract: An example system includes a processor to train a convolutional neural network (CNN) to detect features, and train fully connected layers of the CNN to map detected features to semantic descriptors, based on a data set including one or more lesions. The processor is to also receive a medical image to be analyzed for lesions. The processor is to further extract feature maps from the medical image using the trained CNN. The processor is also to detect a region of interest via the trained CNN and generate a bounding box around the detected region of interest. The processor is to reduce a dimension of the region of interest based on the feature maps. The processor is to generate a semantic description of the region of interest via the trained fully connected layers.
    Type: Grant
    Filed: February 27, 2017
    Date of Patent: November 27, 2018
    Assignee: International Business Machines Corporation
    Inventors: Pavel Kisilev, Eliyahu Sason
  • Patent number: 10115316
    Abstract: A computer implemented method, a computerized system and a computer program product for generating questions. The computer implemented method comprising obtaining a question, wherein the question comprises one or more elements that define an answer for the question. The method further comprising obtaining the answer. The method further comprises automatically generating, by a processor, a new question based on the question and the answer. The automatic generation comprises determining a variant of the one or more elements, wherein the variant defines the answer, wherein the new question comprises the variant.
    Type: Grant
    Filed: July 21, 2014
    Date of Patent: October 30, 2018
    Assignee: International Business Machines Corporation
    Inventors: Ella Barkan, Sharbell Hashoul, Andre Heilper, Pavel Kisilev, Asaf Tzadok, Eugene Walach
  • Publication number: 20180247405
    Abstract: An example system includes a processor to train a convolutional neural network (CNN) to detect features, and train fully connected layers of the CNN to map detected features to semantic descriptors, based on a data set including one or more lesions. The processor is to also receive a medical image to be analyzed for lesions. The processor is to further extract feature maps from the medical image using the trained CNN. The processor is also to detect a region of interest via the trained CNN and generate a bounding box around the detected region of interest. The processor is to reduce a dimension of the region of interest based on the feature maps. The processor is to generate a semantic description of the region of interest via the trained fully connected layers.
    Type: Application
    Filed: February 27, 2017
    Publication date: August 30, 2018
    Inventors: Pavel Kisilev, Eliyahu Sason
  • Publication number: 20180239871
    Abstract: Processing a chief medical complaint, associated with a patient, together with current clinical data items derived from current clinical data associated with the patient to establish a baseline medical diagnosis of the patient, for each of different historical clinical data items derived from historical clinical data associated with the patient, processing the chief medical complaint together with the current clinical data items and the historical clinical data item to establish a comparison medical diagnosis of the patient, where the comparison medical diagnosis results from an diagnostic effect of the historical clinical data item on the baseline medical diagnosis, and determining the diagnostic effect of each of the historical clinical data items on the baseline medical diagnosis, and visually displaying on a visual display medium any of the historical clinical data items in accordance with a prioritization arrangement based on the diagnostic effects of the historical clinical data items.
    Type: Application
    Filed: February 20, 2017
    Publication date: August 23, 2018
    Inventors: Ella Barkan, Sharbell Hashoul, Pavel Kisilev, Eugene Walach
  • Patent number: 9999402
    Abstract: A computer implemented method, a computerized system and a computer program product for automatic image segmentation. The computer implemented method comprises obtaining an image of a tissue, wherein the image is produced using an imaging modality. The method further comprises automatically identifying, by a processor, a tissue segment within the image, wherein said identifying comprises identifying an artifact within the image, wherein the artifact is a misrepresentation of a tissue structure, wherein the misrepresentation is associated with the imaging modality; and searching for the tissue segment in a location adjacent to the artifact.
    Type: Grant
    Filed: July 21, 2014
    Date of Patent: June 19, 2018
    Assignee: International Business Machines Corporation
    Inventors: Dan Chevion, Pavel Kisilev, Boaz Ophir, Eugene Walach
  • Patent number: 9940731
    Abstract: Asymmetries are detected in one or more images by partitioning each image to create a set of patches. Salient patches are identified, and an independent displacement for each patch is identified. The techniques used to identify the salient patches and the displacement for each patch are combined in a function to generate a score for each patch. The scores can be used to identify possible asymmetries.
    Type: Grant
    Filed: September 4, 2015
    Date of Patent: April 10, 2018
    Assignee: International Business Machines Corporation
    Inventors: Sharon Alpert, Miri Erihov, Pavel Kisilev
  • Publication number: 20180096492
    Abstract: Asymmetries are detected in one or more images by partitioning each image to create a set of patches. Salient patches are identified, and an independent displacement for each patch is identified. The techniques used to identify the salient patches and the displacement for each patch are combined in a function to generate a score for each patch. The scores can be used to identify possible asymmetries.
    Type: Application
    Filed: December 6, 2017
    Publication date: April 5, 2018
    Inventors: Sharon Alpert, Miri Erihov, Pavel Kisilev
  • Publication number: 20180060719
    Abstract: Embodiments may provide methods by which fusion of information may be applied to uncertainty reduction in the results of classifier-based data analysis. For example, a method for data analysis may comprise generating a plurality of sets of data samples, each set of data samples representing a portion of input data at plurality of scales, each data sample in a set may represent the portion of the input data at a different scale and location, generating a feature map from each data sample of at least one set of data samples by learning and aggregating features using a first multi-layer convolutional processing, each data sample may be processed with multi-layer convolutional processing separately from other data samples, and generating a feature map by combining the feature maps from the data samples of each set of data samples by performing multiple-scale-multiple-location label fusion using a second multi-layer convolutional processing.
    Type: Application
    Filed: August 29, 2016
    Publication date: March 1, 2018
    Inventors: Pavel Kisilev, Eliyahu Sason
  • Publication number: 20180060487
    Abstract: Presented herein are methods, systems, devices, and computer-readable media for image annotation for medical procedures. The system operates in a parallel manner. In one flow, the system starts from clinical terms and image and applies image detection module in order to get visual candidates for related radiological finding and provide them with semantic descriptors. In the second (parallel) flow, the system produces a list of prioritized semantic descriptors (with probabilities). The second flow is done by application of a reverse inference algorithm that uses clinical terms and expert clinical knowledge. The results of both flows combined by matching module for detection the best candidate and with limited user input images can be annotated. The clinical terms are extracted from clinical documents by textual analysis.
    Type: Application
    Filed: August 28, 2016
    Publication date: March 1, 2018
    Inventors: Ella Barkan, Pavel Kisilev, Eugene Walach
  • Patent number: 9811905
    Abstract: A method comprising using at least one hardware processor for computing a patch distinctiveness score for each of multiple patches of a medical image, computing a shape distinctiveness score for each of multiple regions of the medical image, and computing a saliency map of the medical image, by combining the patch distinctiveness score and the shape distinctiveness score.
    Type: Grant
    Filed: February 8, 2017
    Date of Patent: November 7, 2017
    Assignee: International Business Machines Corporation
    Inventors: Sharon Alpert, Pavel Kisilev
  • Publication number: 20170200092
    Abstract: A computer implemented method of automatically creating a classification function trained with augmented representation of features extracted from a plurality of sample media objects using one or more hardware processors for executing a code. The code comprises code instructions for extracting a plurality of features from a plurality of sample media objects, generating a plurality of feature samples for each of the plurality of features by augmenting the plurality of features, training a classification function with the plurality of features samples and outputting the classification function for classifying one or more new media objects.
    Type: Application
    Filed: January 11, 2016
    Publication date: July 13, 2017
    Inventor: PAVEL KISILEV