公开(公告)号：US20210125074A1

公开(公告)日：2021-04-29

申请号：US16842435

申请日：2020-04-07

Applicant: Lunit Inc.

Inventor： HyunJae LEE ,  Hyo-Eun KIM ,  Weonsuk LEE

IPC: G06N3/08 ,  G06N3/04 ,  G06N20/20 ,  G06T5/50

Abstract: 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.

公开(公告)号：US20240062526A1

公开(公告)日：2024-02-22

申请号：US18384421

申请日：2023-10-27

Applicant: Lunit Inc.

Inventor： Weonsuk LEE ,  Hyeonsoo Lee ,  Gunhee Nam ,  Taesoo Kim

IPC: G06V10/774 ,  G06V10/77 ,  G06V10/82

CPC classification number: G06V10/774 ,  G06V10/7715 ,  G06V10/82 ,  G06V2201/03

Abstract: Provided is a method for training a neural network and a device thereof. The method for training a neural network with three-dimensional (3D) training image data comprising a plurality of two-dimensional (2D) training image data, comprises: training a first convolutional neural network (CNN) with the plurality of 2D training image data, wherein the first convolutional neural network comprises 2D convolutional layers; and training 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.