Abstract: A display apparatus includes a light-emitting panel including a first substrate and a plurality of light-emitting devices disposed on the first substrate, and a color panel including a second substrate, a bank disposed on a lower surface of the second substrate in a direction to the first substrate and including a plurality of first openings each corresponding to emission areas of the plurality of light-emitting devices, a functional layer arranged in the plurality of first openings, a first protrusion pattern disposed on the bank, and a second protrusion pattern disposed on the bank. The first protrusion pattern is arranged between adjacent ones of the plurality of first openings, and the second protrusion pattern is spaced apart from the first protrusion pattern.

Abstract: A lesion diagnosing method includes inputting medical data into first and second neural network models to detect an object region for lesion diagnosis from the medical data, and detect at least one finding region related to a specific lesion; calculating a volume and location for at least one finding region included in the object region; and generating result information for the medical data based on a volume and a location for the finding region.

Abstract: The present disclosure relates to a method for improving reproduction performance of a deep neural network model trained using a group of learning data so that the deep neural network model can exhibit excellent reproduction performance even for target data having a quality pattern different from that of the group, and a device using same. According to the method of the present disclosure, a computing device acquires the target data, retrieves at least one piece of candidate data having a highest similarity to the target data from a learning data representative group including reference data selected from the learning data, performs adaptive pattern transformation on the target data to enable adaptation to the candidate data, and supports transfer of transformed data, which is a result of the adaptive pattern transformation, to the deep neural network model so as to acquire an output value from the deep neural network model.

Abstract: According to an embodiment of the present disclosure, an image analysis method is disclosed. The image analysis method may include: receiving a body medical image; detecting one or more lesions from the received body medical image using a lesion detection model; generating anatomical location information for the one or more lesions using an anatomical analysis model, based on the received body medical image and the detection result for the one or more lesions; and generating a diagnosis result for the received body medical image, based on the anatomical location information for the one or more lesions.

Abstract: According to an embodiment of the present disclosure, disclosed is a method to read a chest image. The method includes: determining whether or not to identify presence of cardiomegaly for a chest image; detecting a lung region and a heart region respectively which are included in the chest image, by using a neural network model, when it is determined to identify presence of cardiomegaly of the chest image; and calculating a cardiothoracic ratio of the chest image using the detected lung region and the detected heart region.

Abstract: Disclosed is a computer program stored in a computer readable storage medium. When the computer program is executed by one or more processors, the computer program provides a user interface for displaying a lesion readings result, and the user interface may include: lesion information comprised in medical data; and one or more findings related to the lesion information and displayed in response to a user interaction with the lesion information.

Abstract: An integrated circuit includes an internal circuit and an attack detection circuit including at least one sensor configured to sense at least one abnormal condition of the internal circuit. The at least one abnormal condition is a parameter of the internal circuit that is outside of a predetermined range. The attack detection circuit is configured to sense an external attack on the internal circuit based on the at least one abnormal condition. The attack detection circuit further includes a security built-in-self-test (BIST) circuit configured to sense a physical attack on the attack detection circuit.