US 12,073,937 B2
Medical information processing apparatus
Shuhei Bannae, Utsunomiya (JP); Maki Minakuchi, Utsunomiya (JP); Sumie Akiyama, Otawara (JP); Hisaaki Oosako, Utsunomiya (JP); and Kohei Shinohara, Nasushiobara (JP)
Assigned to CANON MEDICAL SYSTEMS CORPORATION, Otawara (JP)
Filed by CANON MEDICAL SYSTEMS CORPORATION, Otawara (JP)
Filed on Dec. 21, 2020, as Appl. No. 17/128,883.
Claims priority of application No. 2019-238183 (JP), filed on Dec. 27, 2019.
Prior Publication US 2021/0202070 A1, Jul. 1, 2021
Int. Cl. G06Q 10/00 (2023.01); G06N 3/096 (2023.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01)
CPC G16H 30/20 (2018.01) [G06N 3/096 (2023.01); G16H 30/40 (2018.01)] 9 Claims
OG exemplary drawing
 
1. A medical information processing apparatus, comprising:
processing circuitry configured to
store, in an electronic memory, a first medical information training set that was used to generate a first trained machine-learning model, the first medical information training set including first medical image data obtained from scanning performed using a medical imaging apparatus, the first trained model being trained machine-learning to identify an object in the first medical image data;
calculate, with respect to each second trained machine-learning model of a plurality of second trained machine-learning models being previously trained machine-learning models, a similarity degree between (1) a second medical information training set having been used in training the second trained machine-learning model, and (2) the first medical information training set, the second medical information training set including second medical image data obtained from scanning performed using the medical imaging apparatus, the second trained machine-learning model being trained to identify the object in the second medical image data;
calculate, for each second trained machine-learning model of the plurality of second trained machine-learning models, a data quantity of the first medical information training set that causes, a precision of the second trained machine-learning model to achieve a target precision value by using data extracted from the electronic memory and indicating a correlational relationship between similarity degrees and corresponding data quantities that each achieves the target precision value when training is performed using image data having a corresponding similarity degree, wherein each second trained machine-learning model of the plurality of second trained machine-learning models can be retrained to become a new trained machine-learning model, based on the calculated similarity degree corresponding to the second trained machine-learning model;
display, on a display device, a list of the calculated data quantity for each of the plurality of second trained machine-learning models;
receive selection, by a user, of a particular one of the plurality of second trained machine-learning models displayed in the list of the calculated data quantity for each of the plurality of second trained machine-learning models; and
in response to the selection by the user, re-train the particular one of the plurality of second trained machine-learning models selected by a the user to generate the new trained machine-learning model, using the corresponding calculated data quantity of the first medical information training set as training data.