Abstract: The present application provides an endoscope instrument comprising: a main body which is provided at the top end of an endoscope device, and which can be operated by the operation of an operator; and a light guide unit, which is provided at the main body to emit light forward.

Abstract: Provided are a device, a method, and a system for supporting biometric image finding/diagnosis, the device comprising: a processor; and a memory including one or more instructions implemented to be executed by the processor, wherein the processor; extracts a first attribute information from a first biometric image of an object on the basis of a machine learning model; changes the first attribute information of the first biometric imaged by mapping adversarial noise to the first biometric image, so as to generate a second biometric image having second attribute information; and displays the first biometric image having the first attribute information and the second biometric image having the second attribute information on a display unit.

Abstract: Provided is a device for displaying biological image tissue, comprising a processor and a memory including one or more instructions implemented to be performed by means of the processor, wherein the processor extracts information about a lesion from a first biological image obtained by photographing an object continuously over time on the basis of a machine learning model and processes the first biological image to generate a second biological image including a marker for displaying the information about the lesion, and a display unit for displaying the second biological image in a boundary region between an effective screen and an ineffective screen or in a region of the ineffective screen is included.

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.