-
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.
Inventor: Jongchan Park , Donggeun Yoo
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.
-
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.
-
133.
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.
-
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.
-
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.
Inventor: Kyoung Won LEE , Kyung Hyun PAENG
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.
-
136.
公开(公告)号:US10013757B2
公开(公告)日:2018-07-03
申请号:US15113644
申请日:2015-09-08
Applicant: LUNIT INC.
Inventor: Hyo-eun Kim , Sang-heum Hwang , Seung-wook Paek , Jung-in Lee , Min-hong Jang , Dong-geun Yoo , Kyung-hyun Paeng , Sung-gyun Park
IPC: G06K9/00 , G06T7/00 , G06F19/00 , G06F17/30 , G06N99/00 , G06K9/48 , G06K9/62 , G06K9/66 , G06T7/11 , A61B8/08 , G16H50/20
CPC classification number: G06T7/0012 , A61B6/502 , A61B6/5217 , A61B8/0825 , G06F16/50 , G06F16/583 , G06F19/321 , G06K9/481 , G06K9/6256 , G06K9/6267 , G06K9/6292 , G06K9/66 , G06K2209/05 , G06K2209/053 , G06N20/00 , G06T7/11 , G06T2207/10116 , G06T2207/20021 , G06T2207/20081 , G06T2207/30068 , G06T2207/30096 , G16H30/20 , G16H30/40 , G16H50/20
Abstract: The present invention relates to a classification apparatus for pathologic diagnosis of a medical image and a pathologic diagnosis system using the same. According to the present invention, there is provided a classification apparatus for pathologic diagnosis of a medical image, including: a feature extraction unit configured to extract feature data for an input image using a feature extraction variable; a feature vector transformation unit configured to transform the extracted feature data into a feature vector using a vector transform variable; and a vector classification unit configured to classify the feature vector using a classification variable, and to output the results of the classification of pathologic diagnosis for the input image; wherein the feature extraction unit, the feature vector transformation unit and the vector classification unit are trained based on a first tagged image, a second tagged image, and an image having no tag information.
-
137.
公开(公告)号:US20170061608A1
公开(公告)日:2017-03-02
申请号:US15113680
申请日:2015-09-09
Applicant: LUNIT INC.
Inventor: Hyo-eun KIM , Sang-heum HWANG , Seung-wook PAEK , Jung-in LEE , Min-hong JANG , Dong-geun YOO , Kyung-hyun PAENG , Sung-gyun PARK
CPC classification number: G06T7/0012 , A61B5/7267 , G06F16/51 , G06F16/5838 , G06T2207/10056 , G06T2207/30024 , G16H30/40 , G16H40/60 , G16H50/20 , H04L67/06 , H04L67/10 , H04L67/42
Abstract: The present invention relates to a cloud-based pathological analysis system and method. The present invention provides a cloud-based pathological analysis system, including: a client device coupled to a microscope, and configured to acquire an image for a tissue sample via the microscope and generate a sample image; and a cloud server coupled to the client device over a network, and configured to receive sample image data from the client device over the network and store the sample image data; wherein the cloud server analyzes the received sample image data, and transmits analysis information to the client device.
Abstract translation: 本发明涉及一种基于云的病理分析系统和方法。 本发明提供了一种基于云的病理分析系统,包括:耦合到显微镜的客户端设备,并且被配置为经由显微镜获取组织样本的图像并生成样本图像; 以及云服务器,其通过网络耦合到所述客户端设备,并且被配置为通过网络从所述客户端设备接收样本图像数据并存储所述样本图像数据; 其中云服务器分析接收到的采样图像数据,并将分析信息发送到客户端设备。
-
-
-
-
-
-