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    Systems for automated lesion detection and related methods
    3.
    发明授权
    Systems for automated lesion detection and related methods 有权

    公开(公告)号：US12229949B2

    公开(公告)日：2025-02-18

    申请号：US17401536

    申请日：2021-08-13

    Applicant: Ohio State Innovation Foundation

    Inventor： Engin Dikici ,  Luciano Prevedello ,  Matthew Bigelow

    IPC: G06T7/00 ,  G06F18/21 ,  G06F18/214 ,  G06F18/243 ,  G06N3/04 ,  G06N3/045 ,  G06N3/088 ,  G06T11/60 ,  G06V10/20

    Abstract: Example systems and methods for lesion detection are described herein. An example system includes at least one processor and a memory operably coupled to the at least one processor. The system also includes a candidate selection module configured to receive an image, determine a plurality of candidate points in the image, and select a respective volumetric region centered by each of the candidate points. A portion of a lesion has a high probability of being determined as a candidate point. The system further includes a deep learning network configured to receive the respective volumetric regions selected by the candidate selection module, and determine a respective probability of each respective volumetric region to contain the lesion. Additionally, example methods for training a deep learning network to detect lesions are described herein.

    High-fidelity generative image compression
    4.
    发明授权
    High-fidelity generative image compression 有权

    公开(公告)号：US12225239B2

    公开(公告)日：2025-02-11

    申请号：US18238068

    申请日：2023-08-25

    Applicant: Google LLC

    Inventor： George Dan Toderici ,  Fabian Julius Mentzer ,  Eirikur Thor Agustsson ,  Michael Tobias Tschannen

    IPC: G06V10/00 ,  G06N3/045 ,  G06N3/088 ,  H04N19/124 ,  H04N19/154 ,  H04N19/91

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an encoder neural network configured to receive a data item and to process the data item to output a compressed representation of the data item. In one aspect, a method includes, for each training data item: processing the data item using the encoder neural network to generate a latent representation of the training data item; processing the latent representation using a hyper-encoder neural network to determine a conditional entropy model; generating a compressed representation of the training data item; processing the compressed representation using a decoder neural network to generate a reconstruction of the training data item; processing the reconstruction of the training data item using a discriminator neural network to generate a discriminator network output; evaluating a first loss function; and determining an update to the current values of the encoder network parameters.

    Trade platform with reinforcement learning
    7.
    发明授权
    Trade platform with reinforcement learning 有权

    公开(公告)号：US12198062B2

    公开(公告)日：2025-01-14

    申请号：US18209188

    申请日：2023-06-13

    Applicant: ROYAL BANK OF CANADA

    Inventor： Hasham Burhani ,  Shary Mudassir ,  Xiao Qi Shi ,  Connor Lawless ,  Weiguang Ding

    IPC: G06N3/088 ,  G06N3/02 ,  G06N20/00 ,  G06Q40/04

    Abstract: Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. First and second task data are received. The task data are processed to compute a first performance metric reflective of performance of the automated agent relative to other entities in a first time interval, and a second performance metric reflective of performance of the automated agent relative to other entities in a second time interval. A reward for the reinforcement learning neural network that reflects a difference between the second performance metric and the first performance metric is computed and provided to the reinforcement learning neural network to train the automated agent.

    Deep learning-based anomaly detection in images
    10.
    发明授权
    Deep learning-based anomaly detection in images 有权

    公开(公告)号：US12182721B2

    公开(公告)日：2024-12-31

    申请号：US17913905

    申请日：2021-03-25

    Applicant: YISSUM RESEARCH DEVELOPMENT COMPANY OF THE HEBREW UNIVERSITY OF JERUSALEM LTD.

    Inventor： Yedid Hoshen ,  Liron Bergman ,  Niv Cohen ,  Tal Reiss

    IPC: G06V10/00 ,  G06N3/088 ,  G06V10/44 ,  G06V10/74 ,  G06V10/762 ,  G06V10/82

    Abstract: A method comprising: receiving, as input, training images, wherein at least a majority of the training images represent normal data instances; receiving, as input, a target image; extracting (i) a set of feature representations from a plurality of image locations within each of the training images, and (ii) target feature representations from a plurality of target image locations within the target image; calculating, with respect to a target image location of the plurality of target image locations in the target image, a distance between (iii) the target feature representation of the target image location, and (iv) a subset from the set of feature representations comprising the k nearest the feature representations to the target feature representation; and determining that the target image location is anomalous, when the calculated distance exceeds a predetermined threshold.

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