Apparatus for predicting metadata of medical image and method thereof

    公开(公告)号:US10824908B1

    公开(公告)日:2020-11-03

    申请号:US16708205

    申请日:2019-12-09

    Applicant: Lunit Inc.

    Abstract: This disclosure relates to a computerized method to perform a machine learning on a relationship between medical images and metadata using a neural network and acquiring metadata by applying a machine learning model to medical images, and a method thereof. The apparatus and method may include training a prediction model for predicting metadata of medical images based on multiple medical images for learning and metadata matched with each of multiple medical images and predicting metadata of input medical image.

    NORMALIZATION METHOD FOR MACHINE-LEARNING AND APPARATUS THEREOF

    公开(公告)号:US20200342276A1

    公开(公告)日:2020-10-29

    申请号:US16535314

    申请日:2019-08-08

    Applicant: Lunit Inc.

    Inventor: Jae Hwan LEE

    Abstract: A normalization method for machine learning and an apparatus thereof are provided. The normalization method according to some embodiments of the present disclosure may calculate a value of a normalization parameter for an input image through a normalization model before inputting the input image to a target model and normalize the input image using the calculated value of the normalization parameter. Because the normalization model is updated based on a prediction loss of the target model, the input image can be normalized to an image suitable for a target task, so that stability of the learning and performance of the target model can be improved.

    Method for detecting anomaly using generative adversarial networks, apparatus and system thereof

    公开(公告)号:US10733733B1

    公开(公告)日:2020-08-04

    申请号:US16535277

    申请日:2019-08-08

    Applicant: Lunit Inc.

    Inventor: Hyeon Seob Nam

    Abstract: There is provided an anomaly detection method, apparatus, and system that can improve the accuracy and reliability of a detection result using GAN (Generative Adversarial Networks). An anomaly detection apparatus according to some embodiments includes a memory that stores a GAN-based image translation model and an anomaly detection model, and a processor that translates a learning image with a low-difficulty level into a learning image with a high-difficulty level and learns the anomaly detection model using the translated learning image. The anomaly detection apparatus can improve the detection performance by learning the anomaly detection model with the learning image with the high-difficulty level in which it is difficult detect the anomaly.

    Method for semantic segmentation and apparatus thereof

    公开(公告)号:US10672129B1

    公开(公告)日:2020-06-02

    申请号:US16664468

    申请日:2019-10-25

    Applicant: Lunit Inc.

    Inventor: In Wan Yoo

    Abstract: A semantic segmentation method and apparatus for improving an accuracy of a segmentation result are provided. The semantic segmentation method inputs a labeled image into a segmentation neural network to obtain segmentation information for the image, and back-propagates a segmentation loss for the segmentation information to update the segmentation neural network. The segmentation neural network is updated by further back-propagating an edge loss for the segmentation information.

    METHOD FOR MANAGING ANNOTATION JOB, APPARATUS AND SYSTEM SUPPORTING THE SAME

    公开(公告)号:US20200152316A1

    公开(公告)日:2020-05-14

    申请号:US16671430

    申请日:2019-11-01

    Applicant: Lunit Inc.

    Abstract: A computing device obtains information about a medical slide image, and determines a dataset type of the medical slide image and a panel of the medical slide image. The computing device assigns to an annotator account, an annotation job defined by at least the medical slide image, the determined dataset type, an annotation task, and a patch that is a partial area of the medical slide image. The annotation task includes the determined panel, and the panel is designated as one of a plurality of panels including a cell panel, a tissue panel, and a structure panel. The dataset type indicates a use of the medical slide image and is designated as one of a plurality of uses including a training use of a medical learning model and a validation use of the machine learning model.

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