US 12,087,445 B2
Automatic cervical cancer diagnosis system
Jae Hoon Jeong, Seongnam-si (KR)
Assigned to AIDOT INC., Seoul (KR)
Appl. No. 17/616,336
Filed by AIDOT INC., Seoul (KR)
PCT Filed Dec. 13, 2019, PCT No. PCT/KR2019/017679
§ 371(c)(1), (2) Date Dec. 3, 2021,
PCT Pub. No. WO2020/246676, PCT Pub. Date Dec. 10, 2020.
Claims priority of application No. 10-2019-0066163 (KR), filed on Jun. 4, 2019; and application No. 10-2019-0109333 (KR), filed on Sep. 4, 2019.
Prior Publication US 2022/0328186 A1, Oct. 13, 2022
Int. Cl. G16H 50/20 (2018.01); A61B 5/00 (2006.01); A61B 5/103 (2006.01); G06N 20/00 (2019.01); G16H 30/40 (2018.01); G16H 50/70 (2018.01)
CPC G16H 50/20 (2018.01) [A61B 5/004 (2013.01); A61B 5/1032 (2013.01); A61B 5/4331 (2013.01); A61B 5/7267 (2013.01); G06N 20/00 (2019.01); G16H 30/40 (2018.01); G16H 50/70 (2018.01)] 10 Claims
OG exemplary drawing
 
1. An automatic cervical cancer diagnosis system comprising:
a learning data generator configured to classify unclassified photographed image data for a cervix transmitted from an external device or a storage according to one or a combination of multi-level classification criteria to generate learning data for each new classification criterion in a learning mode;
a photographed image pre-processer configured to pre-process photographed cervix images;
a cervical cancer diagnoser including a machine learning model for cervical cancer that learns a characteristic of the learning data generated for each classification criterion in the learning mode, wherein the machine learning model generates diagnosis information about whether cervical cancer has occurred with respect to the pre-processed photographed cervix image;
a screen display controller configured to display and output a user interface screen configured to display the diagnosis information and inputting evaluation information according to a reading specialist;
a retraining data generator configured to extract information required for retraining from the evaluation information input through the user interface screen and request retraining the machine learning model; and
a diagnosis and evaluation information storage configured to store the diagnosis information about whether cervical cancer has occurred and the evaluation information input through the user interface screen.