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    GENERATION OF ADDITIONAL VIEWS IN BODY PART X-RAY IMAGING
    1.
    发明申请
    GENERATION OF ADDITIONAL VIEWS IN BODY PART X-RAY IMAGING 有权

    公开(公告)号：US20250054137A1

    公开(公告)日：2025-02-13

    申请号：US18719876

    申请日：2022-12-07

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： AXEL SAALBACH ,  HEINRICH SCHULZ ,  ILYAS SIRAZITDINOV

    IPC: G06T7/00 ,  G06T11/00 ,  G16H10/60 ,  G16H50/20

    Abstract: The present invention relates to X-ray imaging. In order to improve X-ray imaging workflow, an image processing apparatus (10) is proposed that comprises an input (12), a processor (14), and an output (16). The input (12) is configured to receive a first X-ray image obtained in an image acquisition. The first X-ray image has a first view of a body part of a patient. The processor (14) is configured to generate, based on the received first X-ray image, a second X-ray image having a second view of the body part of the patient using a pre-trained machine-learning model. The second view is different from the first view. The output (16) is configured to output the generated second X-ray image.

    MULTI-TASK DEEP LEARNING METHOD FOR A NEURAL NETWORK FOR AUTOMATIC PATHOLOGY DETECTION
    7.
    发明申请
    MULTI-TASK DEEP LEARNING METHOD FOR A NEURAL NETWORK FOR AUTOMATIC PATHOLOGY DETECTION 有权

    公开(公告)号：US20220319160A1

    公开(公告)日：2022-10-06

    申请号：US17620142

    申请日：2020-06-25

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： ALEXANDRA GROTH ,  AXEL SAALBACH ,  IVO MATTEO BALTRUSCHAT ,  JENS VON BERG ,  MICHAEL GRASS

    IPC: G06V10/82 ,  G06T7/00 ,  G06V10/774 ,  G06V20/70 ,  G06V10/96

    Abstract: Multi-task deep learning method for a neural network for automatic pathology detection, comprising the steps: receiving first image data (I) for a first image recognition task; receiving (S2) second image data (V) for a second image recognition task; wherein the first image data (I) is of a first datatype and the second image data (V) is of a second datatype, different from the first datatype; determining (S3) first labeled image data (IL) by labeling the first image data (I) and determining second synthesized labeled image data (ISL) by synthesizing and labeling the second image data (V); training (S4) the neural network based on the received first image data (I), the received second image data (V), the determined first labeled image data (IL) and the determined second labeled synthesized image data (ISL); wherein the first image recognition task and the second image recognition task relate to a same anatomic region where the respective image data is taken from and/or relate to a same pathology to be recognized in the respective image data.

    MEDICAL IMAGE DEVICE AND OPERATING METHOD
    8.
    发明申请
    MEDICAL IMAGE DEVICE AND OPERATING METHOD 有权

    公开(公告)号：US20210338185A1

    公开(公告)日：2021-11-04

    申请号：US17285968

    申请日：2019-10-18

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： AXEL SAALBACH ,  TOM BROSCH ,  TIM Philipp HARDER ,  HRISHIKESH NARAYANRAO DESHPANDE ,  EVAN SCHWAB ,  IVO MATTEO BALTRUSCHAT ,  RAFAEL WIEMKER

    IPC: A61B6/00 ,  G16H40/63 ,  G16H40/67 ,  G06N3/08

    Abstract: This application proposes an improved medical imaging device enabling a timely communication of critical findings. The medical imaging device comprises an image acquisition unit, adapted to acquire image data of a subject to be imaged. The medical imaging device further comprises a local data processing device having an artificial-intelligence-module, Al-module, adapted to automatically detect a finding on basis of the acquired image data and to determine a priority status of the detected finding. Further, the medical imaging device comprises a notification module, adapted to provide, if the determined priority status reaches or exceeds a notification threshold, a notification data containing the detected finding. The application further proposes a medical imaging system, a method of operating a medical imaging device, a computer program element and a computer-readable medium having stored the computer program element.

    MEDICAL IMAGING AND ANALYSIS METHOD
    9.
    发明申请
    MEDICAL IMAGING AND ANALYSIS METHOD 有权

    公开(公告)号：US20240404055A1

    公开(公告)日：2024-12-05

    申请号：US18695880

    申请日：2022-09-29

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： HRISHIKESH NARAYANRAO DESHPANDE ,  THOMAS BUELOW ,  AXEL SAALBACH ,  TIM PHILIPP HARDER ,  SHLOMO GOTMAN ,  EDNA COETSER

    IPC: G06T7/00

    Abstract: A method is provided for analyzing survey imaging data. The method comprises acquiring first image data with a first imaging protocol covering a first FOV, processing the data with an anatomical analysis program or routine to detect at least a portion of a target anatomy, and performing a coverage check adapted to determine whether the target anatomy is fully covered within the first FOV. Second image data is subsequently acquired in accordance with a second imaging protocol defining a second FOV. The second imaging protocol may be: the same as the first, but wherein the results of the coverage check are stored and linked with the second image data for later use; different to the first and wherein the results of the coverage check are output to a user interface and a user input responsive thereto is used to determine the second scan protocol; or different to the first, but wherein an adjusted second scan protocol is automatically determined.

    UNCERTAINTY-BASED IN-PRODUCT LEARNING FOR X-RAY
    10.
    发明申请
    UNCERTAINTY-BASED IN-PRODUCT LEARNING FOR X-RAY 有权

    公开(公告)号：US20240395025A1

    公开(公告)日：2024-11-28

    申请号：US18697030

    申请日：2022-09-29

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： DIMITRIOS MAVROEIDIS ,  RICHARD VDOVJAK ,  AXEL SAALBACH ,  MATTHIAS LENGA

    IPC: G06V10/776 ,  G06V10/774 ,  G06V10/82 ,  G16H30/20

    Abstract: A system (SYS) and related method for providing training data. The system is configured to receive a classification result for a class from plural pre-defined classes (Cj). The classification result is produced by a trained machine learning model (M) in response to processing an input image. A decision logic (DL) of the system is configured to analyze input data (pi,qt) comprising the received classification result value (pi) and an uncertainty value (qi) associated with the classification result value. The system outputs, per class, an associated indication whether the input image is or is not useful for re-training the model (M) in respect of the said class.

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