US 12,293,570 B2
Medical image learning method and medical image processing device
Shumpei Kamon, Tokyo (JP)
Assigned to FUJIFILM Corporation, Tokyo (JP)
Filed by FUJIFILM Corporation, Tokyo (JP)
Filed on Jul. 28, 2022, as Appl. No. 17/815,926.
Claims priority of application No. 2021-124718 (JP), filed on Jul. 29, 2021.
Prior Publication US 2023/0029934 A1, Feb. 2, 2023
Int. Cl. G06V 10/774 (2022.01); G06T 7/00 (2017.01); G06V 10/25 (2022.01); G06V 10/40 (2022.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01)
CPC G06V 10/774 (2022.01) [G06T 7/0014 (2013.01); G06V 10/25 (2022.01); G06V 10/40 (2022.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G06T 2207/10068 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30096 (2013.01); G06V 2201/03 (2022.01)] 13 Claims
OG exemplary drawing
 
1. A medical image learning method comprising:
generating a first model through first learning using a first learning image group including a normal image which is a medical image having no region of interest;
inputting an input image group to the first model, the input image group including at least a medical image different from the first learning image group, to perform abnormality detection based on a difference from a reference of the first model;
performing sorting of an extracted image from the input image group, the extracted image being used for learning to prevent erroneous recognition of the region of interest, according to a result of the abnormality detection; and
generating a second model through second learning using a second learning image group including at least the extracted image, the second model detecting a medical image having the region of interest from input medical images.