Abstract: Disclosed is a single photon detection device that comprises a first well, a heavily doped region provided on the first well, a guard ring provided on a side surface of the heavily doped region, and an insulating pattern inserted into the guard ring. The first well has a first conductivity type. The heavily doped region and the guard ring have a second conductivity type different from the first conductivity type.

Abstract: Disclosed is an avalanche photodetection device that comprises a photodetection layer, the photodetection layer includes a first well, a heavily doped region provided on the first well, and an anode contact spaced apart from the heavily doped region, a conductivity type of the first well and the anode contact is p-type, a conductivity type of the heavily doped region is n-type, the heavily doped region is configured to be biased with a positive bias, the anode contact is configured to output a signal.

Abstract: Disclosed is a single-photon avalanche diode comprises a heavily doped region, a first lightly doped region covering the heavily doped region, a guard ring provided on a side surface of the first lightly doped region, a first well covering the first lightly doped region and the guard ring, and a contact electrically connected to the first well. The heavily doped region, the first lightly doped region, and the guard ring have a first conductivity type. The first well and the contact have a second conductivity type.

Abstract: A cylindrical secondary battery includes: an electrode assembly including a first electrode plate and a second electrode plate; a cylindrical case having a disk-shaped top portion and a side portion extending from the top portion, the cylindrical case accommodating the electrode assembly; a cathode terminal extending through the top portion and insulated therefrom; a first current collector plate electrically connected to the first electrode plate and the cathode terminal; a second current collector plate electrically connected to the second electrode plate and the side portion of the cylindrical case; a cap plate coupled to the side portion and insulated therefrom; and an insulation tape between the top portion of the cylindrical case and the first current collector plate and covering the first current collector plate.

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: Disclosed is a single photon detection device comprises a first well, a second well provided on the first well, a heavily doped region provided on the second well, and a contact facing the heavily doped region. The first well, the second well, and the contact have a first conductive type. The heavily doped region has a second conductive type that is different from the first conductive type. The region between the heavily doped region and the contact consist of semiconductor materials.

Abstract: A single-photon detection element includes a substrate including a first surface and a second surface located opposite to each other, and a plurality of plasmonic nanopatterns provided on the second surface, wherein the substrate includes a high-concentration doping region provided adjacent to the first surface, a substrate region provided between the high-concentration doping region and the plurality of plasmonic nanopatterns, and a first well provided between the substrate region and the high-concentration doping region.

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 single-photon detection pixel includes a substrate, a first well provided in the substrate, a pair of heavily doped regions provided on the first well, and a contact provided between the pair of heavily doped regions, wherein the substrate and the pair of heavily doped regions have a first conductivity type, and the first well and the contact have a second conductivity type that is different from the first conductivity type.

Abstract: A single-photon detection device includes a first well having a first conductivity type, a second well provided on the first well and having a second conductivity type that is different from the first conductivity type, a first depletion forming region provided on the second well and having the first conductivity type, a main depletion region provided between the first well and the second well, and a first sub-depletion region provided between the second well and the first depletion forming region, wherein the first well and the first depletion forming region are spaced apart from each other by the second well.

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.