US 12,217,869 B2
Image diagnosis apparatus using deep learning model and method therefor
Mun Yong Yi, Daejeon (KR); Young Jin Park, Daejeon (KR); Jong Kee Chun, Seoul (KR); and Young Sin Ko, Seoul (KR)
Assigned to KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY, Daejeon (KR); and SEEGENE MEDICAL FOUNDATION, Seoul (KR)
Appl. No. 17/624,621
Filed by KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY, Daejeon (KR); and SEEGENE MEDICAL FOUNDATION, Seoul (KR)
PCT Filed Jun. 30, 2020, PCT No. PCT/KR2020/008502
§ 371(c)(1), (2) Date Jan. 4, 2022,
PCT Pub. No. WO2021/006522, PCT Pub. Date Jan. 14, 2021.
Claims priority of application No. 10-2019-0081416 (KR), filed on Jul. 5, 2019.
Prior Publication US 2022/0270756 A1, Aug. 25, 2022
Int. Cl. G16H 50/20 (2018.01); G06N 3/045 (2023.01); G16H 30/20 (2018.01)
CPC G16H 50/20 (2018.01) [G06N 3/045 (2023.01); G16H 30/20 (2018.01)] 20 Claims
OG exemplary drawing
 
1. An image diagnosis apparatus using a deep learning model, comprising:
an image input part receiving a medical image including tissue of a human body;
a classifier classifying the tissue included in the medical image as being one of normal and abnormal in terms of the presence of disease using a trained deep learning model using a weighted loss function in which different weights are assigned to a probability distribution of determining that a feature extracted from the input medical image is abnormal even though the feature is normal and a probability distribution of determining that the feature is normal even though the feature is abnormal; and
a result output part outputting a result of classification by the classifier.