Lunit Inc.

Dong Geun Yoo

Hyun Jae Lee

Jeong Seok Kang

Soo Ick Cho

Sang Hyup Lee

Jong Chan Park

Hyeon Seob Nam

Won Kyung Jung

Ki Hyun You

Min Chul Kim

Jae Hwan Lee

Wonkyung Jung

Kyung Hyun Paeng

Kyunghyun Paeng

Hyo Eun Kim

Jae Hong Aum

Chan-Young Ock

Hae Joon Kim

Sung Gyun Park

Biagio Brattoli

Tai Won Chung

A computing device includes at least one memory, and at least one processor configured to generate, based on first analysis on a pathological slide image, first biomarker expression information, generate, based on a user input for updating at least some of results of the first analysis, second biomarker expression information about the pathological slide image, and control a display device to output a report including medical information about at least some regions included in the pathological slide image, based on at least one of the first biomarker expression information or the second biomarker expression information.

Method and device for processing pathological slide image

A method for operating a medical imaging device includes obtaining lesion information on at least one lesion detected from a medical image, determining a shape and a position of at least one contour corresponding to the at least one lesion based on the obtained lesion information, determining a position of at least one text region that includes a text indicating the lesion information on the at least one lesion in the medical image, and displaying the at least one contour and the text included in the at least one text region on the medical image, based on the determined shape and position of the at least one contour and the determined position of the at least one text region.

Medical imaging device and medical image processing method

Provided is a method for analysing a pathology image, which is performed by at least one processor and includes acquiring a pathology image, inputting the acquired pathology image into a machine learning model and acquiring an analysis result for the pathology image from the machine learning model, and outputting the acquired analysis result, in which the machine learning model is a model trained by using a training data set generated based on a first pathology data set associated with a first domain and a second pathology data set associated with a second domain different from the first domain.

Method and system for analyzing pathology image

The present disclosure relates to a medical image analysis method using a processor and a memory which are hardware. The method includes generating predicted second metadata for a medical image by using a prediction model, and determining a processing method of the medical image based on one of first metadata stored corresponding to the medical image and the second metadata.

Apparatus and method for processing medical image using predicted metadata

A computing apparatus includes: at least one memory; and at least one processor, wherein the processor generates quantitative information regarding at least one cell included in a region of interest of a pathological slide image by analyzing the pathological slide image, generates qualitative information regarding at least one tissue included in the pathological slide image by analyzing the pathological slide image, and controls a display apparatus to output at least one of the quantitative information and the qualitative information on the pathological slide image according to a manipulation of a user.

Method and apparatus for outputting information related to a pathological slide image

A machine learning method that may reduce an annotation cost and may improve performance of a target model is provided. Some embodiments of the present disclosure may provide a machine learning method performed by a computing device, including: acquiring a training dataset of a first model including a plurality of data samples to which label information is not given; calculating a miss-prediction probability of the first model on the plurality of data samples; configuring a first data sample group by selecting at least one data sample from the plurality of data samples based on the calculated miss-prediction probability; acquiring first label information on the first data sample group; and performing first learning on the first model by using the first data sample group and the first label information.

Method and apparatus for machine learning

The present disclosure provides a method for training a neural network that extracts a feature of an image by using data related to a difference between image, and an apparatus using the same. A neural network training method performed by a computing device according to an exemplary embodiment of the present disclosure includes: acquiring a reference image photographed with a first setting with respect to an object and a first comparison image photographed with a second setting with respect to the object; acquiring feature data of the reference image from a first neural network trained by using the reference image; acquiring feature data of a first extract image from a second neural network, wherein the second neural network is trained by using the first extract image formed from data related to a difference between the reference image and the first comparison image; and training a third neural network by using the feature data of the reference image and the feature data of the first extracted image.

Method for neural network training using differences between a plurality of images, and apparatus using the method

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Seoul, South Korea

Dong Geun Yoo