Abstract: An apparatus for providing brain lesion information based on an image includes a magnetic resonance angiography (MRA) provider configured to provide an environment capable of displaying 3D time-of-flight magnetic resonance angiography (3D TOF MRA) using user input, a brain lesion input unit configured to generate and manage a brain lesion image, a maximum intensity projection (MIP) converter configured to configure MIP image data including at least one image frame corresponding to a projection position of the brain lesion image, a noise remover configured to remove noise of brain lesion information and to configure corrected MIP image data, from which the noise is removed, and an MRA reconfiguration unit configured to reconfigure a corrected brain lesion image by back-projecting the corrected MIP image data.

Abstract: An apparatus for evaluating validity of detection of a cancer region may be provided.

Abstract: A deep learning model learning device is proposed, including: a parametric MRI image input part inputting an image corresponding to a diagnosis region, inputting at least one parametric MRI image constructed on the basis of parameters different from each other, and constructing and providing an MRI moving image by using the at least one parametric MRI image; a cancer detection model learning part receiving an input of the at least one parametric MRI image and the MRI moving image corresponding to the diagnosis region, and learning a deep learning model on the basis of information labeling the cancer region; a labeling reference information providing part providing at least one reference information contributing to the labeling of the cancer region; and a labeling processing part checking the cancer region input on the basis of the at least one reference information and processing the labeling of the checked cancer region.

Abstract: A cerebrovascular disease learning apparatus may be provided. The cerebrovascular disease learning apparatus may include a maximum intensity projection magnetic resonance angiography (MIP MRA) configuration unit configured to receive 3D time-of-flight magnetic resonance angiography (3D TOF MRA) and to construct MIP MRA including a plurality of image frames, a space characteristic learning unit configured to construct a space characteristic learning model, a frame characteristic learning unit configured to receive the space characteristics and to construct a frame characteristic learning model based on a recurrent neural network (RNN) learning method, and a lesion characteristic learning unit configured to receive the frame characteristics and to construct a lesion characteristic learning model based on the CNN learning method.

Abstract: An apparatus for providing brain lesion information based on an image includes a magnetic resonance angiography (MRA) provider configured to provide an environment capable of displaying 3D time-of-flight magnetic resonance angiography (3D TOF MRA) using user input, a brain lesion input unit configured to generate and manage a brain lesion image, a maximum intensity projection (MIP) converter configured to configure MIP image data including at least one image frame corresponding to a projection position of the brain lesion image, a noise remover configured to remove noise of brain lesion information and to configure corrected MIP image data, from which the noise is removed, and an MRA reconfiguration unit configured to reconfigure a corrected brain lesion image by back-projecting the corrected MIP image data.

Abstract: A deep learning model learning device is proposed, including: a parametric MRI image input part inputting an image corresponding to a diagnosis region, inputting at least one parametric MRI image constructed on the basis of parameters different from each other, and constructing and providing an MRI moving image by using the at least one parametric MRI image; a cancer detection model learning part receiving an input of the at least one parametric MRI image and the MRI moving image corresponding to the diagnosis region, and learning a deep learning model on the basis of information labeling the cancer region; a labeling reference information providing part providing at least one reference information contributing to the labeling of the cancer region; and a labeling processing part checking the cancer region input on the basis of the at least one reference information and processing the labeling of the checked cancer region.

Abstract: An apparatus for evaluating validity of detection of a cancer region may be provided.

Abstract: An apparatus for image analysis includes: an image acquisition unit for stacking a plurality of two-dimensional image data in a predetermined order; a three-dimensional image generation unit for generating a plurality of three-dimensional data on the basis of different types of multiple items of information for the plurality of two-dimensional image data in a stacked form from the image acquisition unit; and a deep learning algorithm analysis unit for applying a two-dimensional convolutional neural network to each of the plurality of three-dimensional data from the three-dimensional image generation unit, and combining results of applying the two-dimensional convolutional neural network to the plurality of three-dimensional data.

Abstract: An apparatus for image analysis includes: an image acquisition unit for stacking a plurality of two-dimensional image data in a predetermined order; a three-dimensional image generation unit for generating a plurality of three-dimensional data on the basis of different types of multiple items of information for the plurality of two-dimensional image data in a stacked form from the image acquisition unit; and a deep learning algorithm analysis unit for applying a two-dimensional convolutional neural network to each of the plurality of three-dimensional data from the three-dimensional image generation unit, and combining results of applying the two-dimensional convolutional neural network to the plurality of three-dimensional data.

Abstract: A stroke diagnosis and prognosis prediction system includes: an image acquisition unit configured so as to receive a plurality of images including at least a part of a human brain; an image alignment unit for aligning the plurality of images on the basis of a standard brain image; a lesion area detection and mapping unit for respectively detecting lesion areas from the plurality of images, and mapping the detected lesion areas so as to generate one mapping image; a matching and correction unit, which scales a mapping image so as to match the same to the standard brain image and performs image correction on the mapping image; a three-dimensional image generation unit storing the mapping image in a three-dimensional data space, thereby generating a three-dimensional lesion image; and a stroke diagnosis unit for diagnosing a stroke on the basis of the three-dimensional lesion image.

Abstract: There is disclosed a method and system for clinical effectiveness evaluation of artificial intelligence based medical devices. The clinical effectiveness evaluation system includes a diagnosis result receiving unit receiving diagnosis results for arbitrary clinical data from a plurality of specialists and the artificial intelligence based medical device; a correspondence degree calculation unit calculating a degree of correspondence between the received diagnosis results; a statistical value derivation unit deriving a predetermined statistical value on the basis of the calculated degree of correspondence; and a comparison test unit performing a comparison test on the artificial intelligence based medical device using the derived statistical value.

Abstract: A stroke diagnosis and prognosis prediction system includes: an image acquisition unit configured so as to receive a plurality of images including at least a part of a human brain; an image alignment unit for aligning the plurality of images on the basis of a standard brain image; a lesion area detection and mapping unit for respectively detecting lesion areas from the plurality of images, and mapping the detected lesion areas so as to generate one mapping image; a matching and correction unit, which scales a mapping image so as to match the same to the standard brain image and performs image correction on the mapping image; a three-dimensional image generation unit storing the mapping image in a three-dimensional data space, thereby generating a three-dimensional lesion image; and a stroke diagnosis unit for diagnosing a stroke on the basis of the three-dimensional lesion image.