公开(公告)号：US20200242470A1

公开(公告)日：2020-07-30

申请号：US16756182

申请日：2018-10-16

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Vlado Menkovski ,  Asif Rahman ,  Caroline Denise Francoise Raynaud ,  Bryan Conroy ,  Dimitrios Mavroeidis ,  Erik Bresch ,  Teun van den Heuvel

IPC: G06N3/08 ,  G06N20/00 ,  G06K9/62

Abstract: A method, system and computer-program product for identifying neural network inputs for a neural network that may have been incorrectly processed by the neural network. A set of activation values (of a subset of neurons of a single layer) associated with a neural network input is obtained. A neural network output associated with the neural network input is also obtained. A determination is made as to whether a first and second neural network input share similar sets of activation values, but dissimilar neural network outputs or vice versa. In this way a prediction can be made as to whether one of the first and second neural network inputs has been incorrectly processed by the neural network.

公开(公告)号：US12223722B2

公开(公告)日：2025-02-11

申请号：US17057353

申请日：2018-05-25

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Christine Menking Swisher ,  Purnima Rajan ,  Asif Rahman ,  Bryan Conroy

IPC: G06K9/00 ,  G06F18/22 ,  G06V10/26 ,  G06V20/52

Abstract: Techniques disclosed herein relate to identifying individuals in digital images. In some embodiments, a digital image may be acquired (802) that captures an environment containing at least a first subject. A first portion of the digital image depicting the first subject may be segmented (806) into a plurality of superpixels. For each superpixel of the plurality of superpixels: a semantic label may be assigned (810) to the superpixel; features of the superpixel may be extracted (812); and a measure of similarity between the features extracted from the superpixel and features extracted from a reference superpixel identified in a reference digital image may be determined (814), wherein the reference superpixel has a reference semantic label that matches the semantic label assigned to the superpixel. Based on the measures of similarity associated with the plurality of superpixels, it may be determined (818) that the first subject is depicted in the reference image.

公开(公告)号：US12147428B2

公开(公告)日：2024-11-19

申请号：US18282813

申请日：2022-04-02

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Asif Rahman ,  Bryan Conroy ,  Yale Chang

IPC: G06F16/2455 ,  G06F16/22 ,  G06F16/2453 ,  G06F16/248 ,  G16H10/60

Abstract: A method (100) for identifying time series data using a time series retrieval system (800), comprising: receiving (120) a plurality of time series, each time series comprising a plurality of datapoints, wherein a least some of the plurality of times series comprise datapoints obtained at irregular time intervals within the time period; storing (130) the received plurality of time series in a database; generate (140) a context vector for each of the plurality of time series; receiving (150) a request for identification of one or more of the plurality of time series based on similarity to a time series query; identifying (160), based on similarity to the query time series context vector, one or more of the stored generated context vectors; retrieving (170) each time series associated with the identified one or more stored generated context vectors; and providing (180) the retrieved time series.

公开(公告)号：US11676733B2

公开(公告)日：2023-06-13

申请号：US16955090

申请日：2018-12-18

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Bryan Conroy ,  Minnan Xu ,  Asif Rahman ,  Cristhian Mauricio Potes Blandon

IPC: G16H50/70 ,  G16H50/20 ,  G06N20/00 ,  G06F18/22 ,  G06F18/214

CPC classification number: G16H50/70 ,  G06F18/214 ,  G06F18/22 ,  G06N20/00 ,  G16H50/20

Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. In various embodiments, a first value for a query entity may be displayed (702) on an interface. The first value may be related to a first context. A first trained similarity function may be selected (704) from a plurality of trained similarity functions. The first trained similarity function may be associated with the first context. The first selected trained similarity function may be applied (706) to a set of features associated with the query entity and respective sets of features associated with a plurality of candidate entities. A set of one or more similar candidate entities may be selected (708) from the plurality of candidate entities based on application of the first trained similarity function. Information associated with the first set of one or more similar candidate entities may be displayed (710) on the interface.

公开(公告)号：US11468323B2

公开(公告)日：2022-10-11

申请号：US16756182

申请日：2018-10-16

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Vlado Menkovski ,  Asif Rahman ,  Caroline Denise Francoise Raynaud ,  Bryan Conroy ,  Dimitrios Mavroeidis ,  Erik Bresch ,  Teun van den Heuvel

IPC: G06K9/62 ,  G06N3/08 ,  G06N20/00

Abstract: A method, system and computer-program product for identifying neural network inputs for a neural network that may have been incorrectly processed by the neural network. A set of activation values (of a subset of neurons of a single layer) associated with a neural network input is obtained. A neural network output associated with the neural network input is also obtained. A determination is made as to whether a first and second neural network input share similar sets of activation values, but dissimilar neural network outputs or vice versa. In this way a prediction can be made as to whether one of the first and second neural network inputs has been incorrectly processed by the neural network.

公开(公告)号：US20190192110A1

公开(公告)日：2019-06-27

申请号：US16330252

申请日：2017-09-07

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Saman Parvaneh ,  Cristhian Mauricio Potes Blandon ,  Asif Rahman ,  Bryan Conroy

IPC: A61B7/04 ,  A61B5/00 ,  G06N3/08 ,  G06N3/04 ,  G16H50/20

CPC classification number: A61B7/04 ,  A61B5/725 ,  A61B5/7267 ,  A61B5/7278 ,  A61B5/7282 ,  A61B7/00 ,  G06N3/04 ,  G06N3/08 ,  G16H50/20

Abstract: Various embodiments of the inventions of the present disclosure provide a combination of feature-based approach and deep learning approach for distinguishing between normal heart sounds and abnormal heart sounds. A feature-based classifier (60) is applied to a phonocardiogram (PCG) signal to obtain a feature-based abnormality classification of the heart sounds represented by the PCG signal and a deep learning classifier (70) is also applied to the PCG signal to obtain a deep learning abnormality classification of the heart sounds represented by the PCG signal. A final decision analyzer (80) is applied to the feature-based abnormality classification and the deep learning abnormality classification of the heart sounds represented by the PCG signal to determine a final abnormality classification decision of the PCG signal.

公开(公告)号：US20190133480A1

公开(公告)日：2019-05-09

申请号：US16178853

申请日：2018-11-02

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Asif Rahman ,  Bryan Conroy

IPC: A61B5/0468 ,  A61B5/00 ,  G06N5/04 ,  G06N3/08 ,  G06F17/16 ,  G06N3/04

Abstract: Techniques described herein relate to training and applying predictive models using discretized physiological sensor data. In various embodiments, a continuous stream of samples measured by a physiological sensor may be discretized into a training sequence of quantized beats. A training sequence of vectors determined based on the training sequence of quantized beats and an embedding matrix may be associated with labels indicative of medical conditions, and applied as input across a neural network to generate corresponding instances of training output. Based on a comparison of each instance of training output with a respective label, the neural network and the embedding matrix may be trained and used to predict medical conditions from unlabeled continuous streams of physiological sensor samples. In some embodiments, the trained embedding matrix may be visualized to identify correlations between medical conditions and physiological signs.

公开(公告)号：US11627905B2

公开(公告)日：2023-04-18

申请号：US16641824

申请日：2018-09-18

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Jonathan Rubin ,  Saman Parvaneh ,  Asif Rahman ,  Bryan Conroy ,  Saeed Babaeizadeh

IPC: A61B5/361 ,  G16H50/20 ,  A61B5/00 ,  A61B5/35 ,  A61B5/339

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (20) to perform an atrial fibrillation (AF) detection method (100). The method includes: generating a time-frequency representation of an electrocardiogram (ECG) signal acquired over a time interval; processing the time-frequency representation using a neural network (NN) (32) to output probabilities for rhythms of a set of rhythms including at least atrial fibrillation; assigning a rhythm for the ECG signal based on the probabilities for the rhythms of the set of rhythms output by the neural network; and controlling a display device (24) to display the rhythm assigned to the ECG signal.

公开(公告)号：US20200051696A1

公开(公告)日：2020-02-13

申请号：US16533912

申请日：2019-08-07

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Claire Zhao ,  Jonathan Rubin ,  Bryan Conroy ,  Asif Rahman ,  Minnan Xu

IPC: G16H50/30 ,  G16H10/60 ,  G16H50/70

Abstract: A method of determining the infection risk probability for a patient, including: encoding physiological data of the patient into a first synthetic image; encoding environmental data of the patient into a second synthetic image; determining an intrinsic probability of infection for the patient based upon the first synthetic image and the second synthetic image using a machine learning model; generating a graphical model based upon the patient and other patients based upon similarity scores between the patient and the other patients; and determining the infection risk probability for the patient based upon the graphical model and the intrinsic probability of infection for the patient and the other patients.

公开(公告)号：US11875277B2

公开(公告)日：2024-01-16

申请号：US17477615

申请日：2021-09-17

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Bryan Conroy ,  Minnan Xu ,  Asif Rahman ,  Cristhian Mauricio Potes Blandon

IPC: G06N5/04 ,  G06N5/048 ,  G06N20/00 ,  G16H50/30 ,  G16H20/17 ,  G06N20/10 ,  G06N5/022 ,  G16H40/63 ,  G16H50/70 ,  G16H50/20

CPC classification number: G06N5/048 ,  G06N5/022 ,  G06N20/00 ,  G06N20/10 ,  G16H20/17 ,  G16H40/63 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70

Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions (118) may be provided (602). Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function (120) may be provided (604) as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided (606) as context training data. An approximation function may be applied (608) to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained (610) based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.