Abstract: Provided are apparatuses, a non-transitory computer-readable medium or media, and methods for supporting predicting of vascular disease using a fundus image of a subject. In certain aspects, disclosed a method including the steps of: extracting a feature information from a first fundus image of the subject based on a machine learning model; generating a second fundus image having a feature which is corresponding to the feature information by mapping a saliency factor to the first fundus image; and displaying the first fundus image and the second fundus image on a display device.

Abstract: Provided are apparatuses, a non-transitory computer-readable medium or media, and methods for supporting reading of a fundus image of a subject. In certain aspects, disclosed a method including the steps of: extracting a first feature information from a first fundus image of the subject based on a machine learning model; generating a second fundus image having a second feature information by mapping an adversarial factor to the first fundus image so that the first feature information is changed; and displaying the first fundus image having the first feature information and the second fundus image having the second feature information on a display device.

Abstract: Provided are a computing device and methods for identifying real-time biometric image. In certain aspects, disclosed a method including the steps of: extracting a first feature information from a nth(n is a natural number) biometric image among biometric images continuously photographed temporally of an object based on a machine learning model; generating a fusion data using at least one sensor data among sensor data temporally corresponding to a n+1th or more biometric images and the first feature information of the nth biometric image; and extracting a second feature information of the n+1th or more biometric images from the fusion data based on a second machine learning model. This present disclosure application is a result developed through the Seoul Industry Promotion Agency's 2021 technology commercialization support project (TB210264), “Improvement and advancement of an explainable artificial intelligence prototype that detects major organs during laparoscopic surgery”.

Abstract: Provided are apparatuses, a non-transitory computer-readable medium or media, and methods for supporting predicting of vascular disease using a fundus image of a subject. In certain aspects, disclosed a method including the steps of: extracting a feature information from a first fundus image of the subject based on a machine learning model; generating a second fundus image having a feature which is corresponding to the feature information by mapping a saliency factor to the first fundus image; and displaying the first fundus image and the second fundus image on a display device.

Abstract: Provided are apparatuses, a non-transitory computer-readable medium or media, and methods for supporting reading of a fundus image of a subject. In certain aspects, disclosed a method including the steps of: extracting a first feature information from a first fundus image of the subject based on a machine learning model; generating a second fundus image having a second feature information by mapping an adversarial factor to the first fundus image so that the first feature information is changed; and displaying the first fundus image having the first feature information and the second fundus image having the second feature information on a display device.