公开(公告)号：US20180144209A1

公开(公告)日：2018-05-24

申请号：US15378039

申请日：2016-12-14

Applicant: Lunit Inc.

Inventor： Hyo Eun KIM ,  Sang Heum HWANG

IPC: G06K9/46 ,  G06T7/00 ,  G06N3/08 ,  G06N3/04 ,  G06F19/00

CPC classification number: G06K9/4671 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/08 ,  G06T7/0012 ,  G06T2207/10004 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30096 ,  G16H50/20

Abstract: Provided are an object recognition method and apparatus which determine an object of interest included in a recognition target image using a trained machine learning model and determine an area in which the object of interest is located in the recognition target image. The object recognition method based on weakly supervised learning, performed by an object recognition apparatus, includes extracting a plurality of feature maps from a training target image given classification results of objects of interest, generating an activation map for each of the objects of interest by accumulating the feature maps, calculating a representative value of each of the objects of interest by aggregating activation values included in a corresponding activation map, determining an error by comparing classification results determined using the representative value of each of the objects of interest with the given classification results and updating a CNN-based object recognition model by back-propagating the error.

公开(公告)号：US20220415013A1

公开(公告)日：2022-12-29

申请号：US17895315

申请日：2022-08-25

Applicant: LUNIT INC.

Inventor： Dong Geun YOO ,  Min Chul KIM ,  Hyo Eun KIM ,  Hyun Jae LEE ,  Jae Hwan LEE ,  Hae Joon KIM

IPC: G06V10/46 ,  G06V10/25 ,  G06V10/44 ,  G06V10/74 ,  G06T7/70 ,  G06T11/20

Abstract: 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.

公开(公告)号：US20180060722A1

公开(公告)日：2018-03-01

申请号：US15378001

申请日：2016-12-13

Applicant: Lunit Inc.

Inventor： Sang Heum HWANG ,  Hyo Eun KIM

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/0454 ,  G06N3/084

Abstract: Provided are a machine learning method based on weakly supervised learning includes extracting feature maps about a dataset given a first type of information and not given a second type of information by using a convolutional neural network (CNN), updating the CNN by back-propagating a first error value calculated as a result of performing a task corresponding to the first type of information by using a first model, and updating the CNN by back-propagating a second error value calculated as a result of performing the task corresponding to the first type of information by using a second model different from the first model, wherein the second type of information is extracted when the task corresponding to the first type of information is performed using the second model.

公开(公告)号：US20200302286A1

公开(公告)日：2020-09-24

申请号：US16438776

申请日：2019-06-12

Applicant: Lunit Inc.

Inventor： Hyeon Seob NAM ,  Hyo Eun KIM

IPC: G06N3/08

Abstract: 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.