US 12,254,983 B2
Electronic device and method of training classification model for age-related macular degeneration
Meng-Che Cheng, New Taipei (TW); Ming-Tzuo Yin, New Taipei (TW); and Yi-Ting Hsieh, Taipei (TW)
Assigned to Acer Medical Inc., New Taipei (TW)
Filed by Acer Medical Inc., New Taipei (TW)
Filed on Sep. 2, 2021, as Appl. No. 17/464,683.
Claims priority of application No. 110120194 (TW), filed on Jun. 3, 2021.
Prior Publication US 2022/0392635 A1, Dec. 8, 2022
Int. Cl. G06N 5/04 (2023.01); G06F 17/18 (2006.01); G16H 50/20 (2018.01)
CPC G16H 50/20 (2018.01) [G06F 17/18 (2013.01)] 8 Claims
OG exemplary drawing
 
1. An electronic device for training a classification model for age-related macular degeneration, comprising:
a transceiver; and
a processor, coupled to the transceiver, wherein the processor is configured to:
obtaining training data through the transceiver;
calculate a loss function vector corresponding to the training data based on a machine learning algorithm, wherein the loss function vector comprises a first loss function value corresponding to a first classification of the age-related macular degeneration and a second loss function value corresponding to a second classification of the age-related macular degeneration, the first classification corresponds to a first group, and the second classification corresponds to one of the first group and a second group, wherein the first classification and the second classification respectively correspond to different stages of the age-related macular degeneration;
generate a first penalty weight based on a first stage difference between the first classification and the second classification, wherein the first penalty weight is proportional to the stage difference;
update the first loss function value according to the second loss function value, the first penalty weight, and a group penalty weight in response to the second classification corresponding to the second group, so as to generate an updated loss function vector; and
train the classification model according to the updated loss function vector.