Abstract: A method and a system for medical image interpretation are provided. A medical image is provided to a convolutional neural network model. The convolutional neural network model includes a feature extraction part, a first classifier, and N second classifiers. N feature maps are generated by using the last layer of the feature extraction part of the convolutional neural network model. N symptom interpretation results of N symptoms of a disease are obtained based on the N feature maps through the N second classifiers. A disease interpretation result of the disease is obtained based on the N feature maps through the first classifier.

Abstract: A method and an apparatus for compensating image segmentation lines are provided. In the method, image segmentation is applied to a medical image captured by using a segmentation model to obtain a segmentation image including at least one segmentation line between multiple layers in the medical image. Convolution computation is then performed on the segmentation image by using a kernel of a trained classification model to predict a location of a next pixel connected to a current pixel in the respective segmentation line within the segmentation image, in which the pixel to be predicted is limited to a neighboring pixel of the current pixel in a prediction direction. The predicted pixels are connected to form a compensated segmentation line for each segmentation line.

Abstract: An image processing method is provided. In the image processing method, a plurality of image quality assessment methods are received, a total execution time of a cloud server for executing image quality assessment methods is calculated, and individual execution times of an image processing device for executing image quality assessment are respectively calculated. The image quality assessment methods are classified into first type of image quality assessment methods and second type of image quality assessment methods according to the individual execution times and the total execution time. The first type of image quality assessment method are performed by the image processing device, and the second type of image quality assessment method are performed by the cloud server. Accordingly, an efficiency of image recognition may be improved by the image processing method classifying the image quality assessment methods.

Abstract: An electronic device and a method of training a classification model for age-related macular degeneration (AMD) are provided. The method includes the following steps. Training data is obtained. A loss function vector corresponding to the training data is calculated based on a machine learning algorithm, in which the loss function vector includes a first loss function value corresponding to a first stage of AMD and a second loss function value corresponding to a second stage of AMD. A first penalty weight is generated according to a stage difference between the first stage and the second stage. The first loss function value is updated according to the second loss function value and the first penalty weight, so as to generate an updated loss function vector. The classification model is trained according to the updated loss function vector.

Abstract: An electronic device and a method of training a classification model for age-related macular degeneration (AMD) are provided. The method includes the following steps. Training data is obtained. A loss function vector corresponding to the training data is calculated based on a machine learning algorithm, in which the loss function vector includes a first loss function value corresponding to a first classification of AMD and a second loss function value corresponding to a second classification of AMD, the first classification corresponds to a first group, and the second classification corresponds to one of the first group and a second group. The first loss function value is updated according to the second loss function value and a group penalty weight in response to the second classification corresponding to the second group to generate an updated loss function vector. The classification model is trained according to the updated loss function vector.

Abstract: A method and a system for medical image interpretation are provided. A medical image is provided to a convolutional neural network model. The convolutional neural network model includes a feature extraction part, a first classifier, and N second classifiers. N feature maps are generated by using the last layer of the feature extraction part of the convolutional neural network model. N symptom interpretation results of N symptoms of a disease are obtained based on the N feature maps through the N second classifiers. A disease interpretation result of the disease is obtained based on the N feature maps through the first classifier.

Abstract: A classification method and a classification device for classifying a level of an age-related macular degeneration are provided. The classification method includes the following. An object detection model and a first classification model are pre-stored. A fundus image is obtained. A bounding box is generated in the fundus image according to a macula in the fundus image detected by the object detection model. An intersection over union between a predetermined area and the bounding box in the fundus image is calculated. A classification of the fundus image is generated according to the first classification model in response to the intersection over union being greater than a threshold.