Lunit Inc.

S-printing Solution Co., Ltd.

Hyo-Eun Kim

Hyeonseob Nam

HyunJae Lee

Tae-gyun Cho

Dong-kyu Heo

Ju-ho Eum

Sang-heum Hwang

Jung-in Lee

Min-hong Jang

Dong-geun Yoo

Kyung-hyun Paeng

Sung-gyun Park

Weonsuk Lee

Seung-wook Paek

A method for interpreting an input image by a computing device operated by at least one processor is provided. The method for interpreting an input image comprises storing an artificial intelligent (AI) model that is trained to classify a lesion detected in the input image as suspicious or non-suspicious and, under a condition of being suspicious, to classify the lesion detected in the input image as malignant or benign-hard representing that the lesion is suspicious but determined to be benign, receiving an analysis target image, by using the AI model, obtaining a classification class of a target lesion detected in the analysis target image and, when the classification class is the suspicious, obtaining at least one of a probability of being suspicious, a probability of being benign-hard, and a probability of malignant for the target lesion, and outputting an interpretation result including at least one probability obtained for the target lesion.

Method for discriminating suspicious lesion in medical image, method for interpreting medical image, and computing device implementing the methods

Provided is a method for training a neural network and a device thereof. The method may train a neural network with three-dimensional (3D) training image data including a plurality of two-dimensional (2D) training image data. The method may include training, at a processor, a first convolutional neural network (CNN) with the plurality of 2D training image data, wherein the first convolutional neural network comprises 2D convolutional layers. The method may further include training, at the processor, a second convolutional neural network with the 3D training image data, wherein the second convolutional neural network comprises the 2D convolutional layers and 3D convolutional layers configured to receive an output of the 2D convolutional layers as an input.

Method for training neural network and device thereof

This disclosure relates to a method of sequential machine learning of data sets and an apparatus thereof. The method may include generating a first machine learning model by generating a first feature space based on a first data set, generating first predictive label information based on the first feature space, performing machine learning on a relationship between the first data set and first label information related to a first data set, and performing machine learning on a relationship between the first predictive label information and the first feature space. The method may also include generating a second machine learning model based on the first machine learning model by generating a second feature space based on a second data set, generating second predictive label information based on the second feature space, and performing machine learning on a relationship between the second data set and a second label information.

Method of continual-learning of data sets and apparatus thereof

An image forming apparatus for executing an application for performing an image forming operation is provided. The apparatus includes an interface library. The interface library includes information about an image forming operation performable by the image forming apparatus, a user interface (UI) configured to provide a UI screen for receiving a request to perform the image forming operation, by referring to the interface library, at least one operation processor configured to call an application programming interface (API) corresponding to the image forming operation, based on the received request to perform the image forming operation, and an operation performer configured to perform the image forming operation requested to be performed, according to the call.

Image forming apparatus executing application for performing image forming operation, and method of performing image forming operation

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.

Classification apparatus for pathologic diagnosis of medical image, and pathologic diagnosis system using the same

A system which provides a solution service to an image forming apparatus and includes a solution server having a solution application installed therein, an image forming apparatus configured to provide a user interface (UI) for the solution application and perform a job requested by the solution server, and a message server configured to communicate with the solution server and the image forming apparatus. The solution server transmits a job request to the message server by using a message protocol, and the image forming apparatus receives the job request by accessing the message server.

Growing community of inventors

Seoul, South Korea

Hyo-Eun Kim