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    Systems and methods for training generative adversarial networks and use of trained generative adversarial networks
    12.
    发明授权
    Systems and methods for training generative adversarial networks and use of trained generative adversarial networks 有权

    公开(公告)号：US11195052B2

    公开(公告)日：2021-12-07

    申请号：US16833451

    申请日：2020-03-27

    Applicant: COSMO ARTIFICIAL INTELLIGENCE—AI LIMITED

    Inventor： Nhan Ngo Dinh ,  Giulio Evangelisti ,  Flavio Navari

    IPC: G06K9/00 ,  G06K9/62 ,  A61B1/00 ,  A61B1/273 ,  G06N3/08 ,  G06T7/00 ,  G16H50/20 ,  G16H30/40 ,  A61B1/31 ,  G06N3/04

    Abstract: The present disclosure relates to computer-implemented systems and methods for training and using generative adversarial networks to detect abnormalities in images of a human organ. In one implementation, a method is provided for training a neural network system, the method may include applying a perception branch of an object detection network to frames of a first subset of a plurality of videos to produce a first plurality of detections of abnormalities. Further, the method may include using the first plurality of detections and frames from a second subset of the plurality of videos to train a generator network to generate a plurality of artificial representations of polyps, and training an adversarial branch of the discriminator network to differentiate between artificial representations of the abnormalities and true representations of abnormalities. Additionally, the method may include retraining the perception branch based on difference indicators between the artificial representations of abnormalities and true representations of abnormalities included in frames of the second subset of plurality of videos and a second plurality of detections.

    Computer-implemented systems and methods for analyzing examination quality for an endoscopic procedure
    14.
    发明授权
    Computer-implemented systems and methods for analyzing examination quality for an endoscopic procedure 有权

    公开(公告)号：US12190512B2

    公开(公告)日：2025-01-07

    申请号：US17961764

    申请日：2022-10-07

    Applicant: Cosmo Artificial Intelligence—AI Limited

    Inventor： Andrea Cherubini ,  Pietro Salvagnini ,  Nhan Ngo Dinh

    IPC: G06T15/00 ,  A61B1/00 ,  G06T7/00

    Abstract: A computer-implemented system is provided that includes at least one processor that is adapted to analyze a plurality of frames from a real-time video to identify frames during which an operator is interacting with an image device to examine areas of a patient. The at least one processor is further configured to generate, from the identified frames, data representations of a first area examined by the operator interacting with the image device and further generate data representations of one or more further areas examined by the operator interacting with the image device. The at least one processor is also configured to aggregate the data representations of the first area with the data representations of the one or more further areas and determine, using the aggregated data representations, an examination quality level of the areas examined by the operator and present, on a display device during the medical procedure, a graphical representation indicating the examination quality level of the areas examined by the operator.

    SYSTEMS AND METHODS FOR TRAINING GENERATIVE ADVERSARIAL NETWORKS AND USE OF TRAINED GENERATIVE ADVERSARIAL NETWORKS
    17.
    发明申请
    SYSTEMS AND METHODS FOR TRAINING GENERATIVE ADVERSARIAL NETWORKS AND USE OF TRAINED GENERATIVE ADVERSARIAL NETWORKS 有权

    公开(公告)号：US20210133507A1

    公开(公告)日：2021-05-06

    申请号：US17251773

    申请日：2019-06-11

    Applicant: COSMO ARTIFICIAL INTELLIGENCE - AI LIMITED

    Inventor： Nhan NGO DINH ,  Giulio EVANGELISTI ,  Flavio NAVARI

    IPC: G06K9/62 ,  G06T7/00 ,  G16H30/40 ,  G16H50/20 ,  G06N3/04 ,  G06N3/08 ,  A61B1/273 ,  A61B1/31

    Abstract: The present disclosure relates to computer-implemented systems and methods for training and using generative adversarial networks. In one implementation, a system for training a generative adversarial network may include at least one processor that may provide a first plurality of images including representations of a feature-of-interest and indicators of locations of the feature-of-interest and use the first plurality and indicators to train an object detection network. Further, the processor(s) may provide a second plurality of images including representations of the feature-of-interest, and apply the trained object detection network to the second plurality to produce a plurality of detections of the feature-of-interest. Additionally, the processor(s) may provide manually set verifications of true positives and false positives with respect to the plurality of detections, use the verifications to train a generative adversarial network, and retrain the generative adversarial network using at least one further set of images, further detections, and further manually set verifications.

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