Abstract: An image processing apparatus is provided that includes: an obtaining unit configured to obtain a first medical image of a subject; and an image quality improving unit configured to generate a second medical image with image quality higher than image quality of different regions including a first region and a second region that is different from the first region in the obtained first image, using the obtained first image as input data that is input into a learned model.

Abstract: A medical image processing apparatus includes: an obtaining unit configured to obtain a first image that is a motion contrast en-face image of an eye to be examined; and an image quality improving unit configured to generate a second image with at least one of lower noise and higher contrast than the obtained first image using the obtained first image as input data that is input into an image quality improving engine, wherein the image quality improving engine includes a machine learning engine that has been obtained by using training data including a second image with at least one of lower noise and higher contrast than a first image that is a motion contrast en-face image of an eye to be examined.

Abstract: An information processing system includes first and second information processing apparatuses. The first information processing apparatus includes a first acquisition unit that acquires a first inference model based on a first neural network including an input layer, an intermediate layer group, and an output layer, a first training unit that trains the first inference model using teacher data, and a transmission unit that transmits output information based on forward propagation by the first training unit to the second information processing apparatus. The second information processing apparatus includes a second acquisition unit that acquires a second inference model based on a second neural network including an input layer, an intermediate layer group, and an output layer, wherein the second inference model is a common inference model and is similar to the first inference model, and a second training unit that trains the second inference model based on the output information.

Abstract: An information processing system includes a second information processing apparatus configured to communicate with a first information processing apparatus via a network, an inference target data acquisition unit configured to acquire inference target data, and an inference unit configured to perform predetermined inference processing on the inference target data by using a first partial model and a second partial model. The first information processing apparatus includes the first partial model configured to include a first input layer, a first group of intermediate layers including at least one of the intermediate layers in the group of intermediate layers, and a first output layer, and the second information processing apparatus includes the second partial model configured to include a second group of intermediate layers including an intermediate layer different from the intermediate layers included in the first group of intermediate layers in the group of intermediate layers, and the second output layer.

Abstract: An image processing apparatus includes display control means for displaying, on display means, a blend image obtained by performing blend processing at a variable transmissivity in accordance with an instruction of an operator using an optical coherence tomography (OCT) image and an OCT angiography (OCTA) image of mutually corresponding regions in a subject that are acquired by an OCT, setting means for setting a region of interest in the displayed blend image, and execution means for executing processing on the set region of interest in at least one image of the OCT image and the OCTA image.

Abstract: A medical image processing apparatus includes: an obtaining unit configured to obtain a first image that is a motion contrast en-face image of an eye to be examined; and an image quality improving unit configured to generate a second image with at least one of lower noise and higher contrast than the obtained first image using the obtained first image as input data that is input into an image quality improving engine, wherein the image quality improving engine includes a machine learning engine that has been obtained by using training data including a second image with at least one of lower noise and higher contrast than a first image that is a motion contrast en-face image of an eye to be examined.

Abstract: An image processing apparatus includes display control means for displaying, on display means, a blend image obtained by performing blend processing at a variable transmissivity in accordance with an instruction of an operator using an optical coherence tomography (OCT) image and an OCT angiography (OCTA) image of mutually corresponding regions in a subject that are acquired by an OCT, setting means for setting a region of interest in the displayed blend image, and execution means for executing processing on the set region of interest in at least one image of the OCT image and the OCTA image.

Abstract: The optical coherence tomography apparatus is an optical coherence tomography apparatus that obtains a tomographic image of an eye to be examined by using combined light obtained by combining (a) return light from the eye to be examined irradiated with measurement light and (b) reference light, the optical coherence tomography apparatus including an optical path length difference changing unit arranged to change an optical path length difference between the measurement light and the reference light, a driving unit arranged to drive the optical path length difference changing unit, a determining unit configured to determine, using a learned model, a driving amount of the driving unit from the obtained tomographic image, and a controlling unit configured to control the driving unit using the determined driving amount.

Abstract: An image processing apparatus is provided that includes: an obtaining unit configured to obtain a plurality of images relating to different radiation energies; and a generating unit configured to generate at least one of energy-subtraction images based on the plurality of images using a learned model, wherein the learned model is obtained using a first image obtained using a radiation and a second image obtained by improving image-quality of the first image or by adding a noise which has been artificially calculated to the first image.

Abstract: A radiation imaging apparatus includes a detection unit configured to detect radiation emitted from a radiation irradiation unit, the apparatus comprises a processing unit configured to obtain dose distribution information regarding the radiation with which the detection unit is irradiated from the radiation irradiation unit. The processing unit corrects, using the dose distribution information, an image signal output from the detection unit.

Abstract: The optical coherence tomography apparatus is an optical coherence tomography apparatus that obtains a tomographic image of an eye to be examined by using combined light obtained by combining (a) return light from the eye to be examined irradiated with measurement light and (b) reference light, the optical coherence tomography apparatus including an optical path length difference changing unit arranged to change an optical path length difference between the measurement light and the reference light, a driving unit arranged to drive the optical path length difference changing unit, a determining unit configured to determine, using a learned model, a driving amount of the driving unit from the obtained tomographic image, and a controlling unit configured to control the driving unit using the determined driving amount.

Abstract: An information processing apparatus disclosed in the present specification includes: estimation means for estimating a subject's risk of developing a disease using a learned model that has learned a relationship between a feature obtained from a fundus image and a risk of developing the disease, which is evaluated from the feature; and correction means for correcting the estimated risk of developing the disease based on the subject's biological information.

Abstract: An ophthalmic apparatus is provided that includes: an optical head unit; an information obtaining unit that, using a learned model obtained by learning information of a position relating to at least one of an eye to be examined and an optical head unit, obtains information of a position relating to at least one of an eye to be examined and the optical head unit from an image relating to an eye to be examined that is obtained using the optical head unit; and a drive controlling unit that controls driving of at least one of a supporter that supports a face of a subject and the optical head unit; in which, based on the obtained information of the position, the drive controlling unit controls the driving to cause at least one of the eye to be examined and the optical head unit to move to the position.

Abstract: An ophthalmic apparatus includes an inspection unit configured to inspect an eye to be inspected, a driving unit configured to drive the inspection unit, and a control unit configured to start control of the inspection unit and the driving unit based on an inspection protocol in response to a predetermined condition, the inspection protocol defining a series of control procedures for performing a plurality of inspections including an alignment adjustment for aligning the inspection unit with the eye to be inspected, wherein the control unit is configured to display results of the plurality of inspections and display information for accepting an instruction to retry a part of the plurality of inspections on a display unit.

Abstract: An information processing apparatus includes a storage unit configured to store information individually set for each of a plurality of different types of imaging as transmission settings for a plurality of pieces of imaging data obtained by the plurality of different types of imaging, and a transmission unit configured to transmit imaging data on a test subject based on the stored information, the imaging data being obtained by any one of the plurality of different types of imaging.

Abstract: An image processing apparatus includes display control means for displaying, on display means, a blend image obtained by performing blend processing at a variable transmissivity in accordance with an instruction of an operator using an optical coherence tomography (OCT) image and an OCT angiography (OCTA) image of mutually corresponding regions in a subject that are acquired by an OCT, setting means for setting a region of interest in the displayed blend image, and execution means for executing processing on the set region of interest in at least one image of the OCT image and the OCTA image.

Abstract: A medical image processing apparatus includes: an obtaining unit configured to obtain a first image that is a medical image of a predetermined site of a subject; an image quality improving unit configured to generate, from the first image, a second image in which image quality is improved compared to the first image, using an image quality improving engine including a machine learning engine; and a display controlling unit configured to cause a composite image obtained by combining the first image and the second image according to a ratio obtained using information relating to at least a partial region in at least one of the first image and the second image to be displayed on a display unit.

Abstract: An image processing apparatus includes: an obtaining unit configured to obtain a first medical image of an object under examination; an image quality improving unit configured to generate, from the obtained first medical image, a second medical image with image quality higher than image quality of the obtained first medical image using a learned model; a comparing unit configured to compare an analysis result obtained by analyzing the obtained first medical image and an analysis result obtained by analyzing the generated second medical image; and a display controlling unit configured to cause a comparison result obtained by the comparing unit to be displayed on a display unit.

Abstract: An image processing apparatus is provided that includes: an obtaining unit configured to obtain a first medical image of a subject; and an image quality improving unit configured to generate a second medical image with image quality higher than image quality of different regions including a first region and a second region that is different from the first region in the obtained first image, using the obtained first image as input data that is input into a learned model.

Abstract: A medical image processing apparatus includes: an obtaining unit configured to obtain a first image that is a motion contrast en-face image of an eye to be examined; and an image quality improving unit configured to generate a second image with at least one of lower noise and higher contrast than the obtained first image using the obtained first image as input data that is input into an image quality improving engine, wherein the image quality improving engine includes a machine learning engine that has been obtained by using training data including a second image with at least one of lower noise and higher contrast than a first image that is a motion contrast en-face image of an eye to be examined.