Lunit Inc.

Hyeon Seob Nam

Hyun Jae Lee

Sang Hyup Lee

Jong Chan Park

Dong Geun Yoo

Ki Hyun You

Hyo Eun Kim

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

There is provided is a method and an apparatus for training a neural network capable of improving the performance of the neural network by performing intelligent normalization according to a target task of the neural network. The method according to some embodiments of the present disclosure includes transforming the output data into first normalized data using a first normalization technique, transforming the output data into second normalized data using a second normalization technique and generating target normalized data by aggregating the first normalized data and the second normalized data based on a learnable parameter. At this time, a rate at which the first normalization data is applied in the target normalization data is adjusted by the learnable parameter so that the intelligent normalization according to the target task can be performed, and the performance of the neural network can be improved.

Apparatus and method for training neural network by performing normalization using a plurality of normalization techniques

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.

Growing community of inventors

Seoul, South Korea

Hyeon Seob Nam