Abstract: A processor derives a first feature amount for an object included in an image by a first neural network, structures a sentence including description of the object included in the image to derive structured information for the sentence, and derives a second feature amount for the sentence from the structured information by a second neural network. The processor trains the first neural network and the second neural network such that, in a feature space to which the first feature amount and the second feature amount belong, a distance between the derived first feature amount and second feature amount is reduced in a case in which the object included in the image and the object described in the sentence correspond to each other.

Abstract: A processor derives a first feature amount for an object included in an image by a first neural network, and derives a second feature amount for a sentence including description of an object by a second neural network. The processor acquires each of a first attribute, which is an attribute of the object included in the image, and a second attribute, which is an attribute of the sentence. The processor trains the first and second neural networks such that, in a feature space to which first and second feature amounts belong, as relevance of a combination of the first and the second attributes is higher a distance between the derived first feature amount and second feature amount is smaller.

Abstract: An information processing apparatus including at least one processor, wherein the at least one processor is configured to: derive a property score indicating a prominence of a property for each of predetermined property items from at least one image; and derive, for each of the property items, a description score indicating a degree of recommendation for including a description regarding the property item in a document.

Abstract: An information processing apparatus includes at least one processor, and the processor derives a property for at least one predetermined property item which is related to a structure of interest included in an image. The processor specifies a basis region serving as a basis for deriving the property related to the structure of interest for each property item and derives a basis image including the basis region.

Abstract: An information processing apparatus comprising at least one processor, wherein the at least one processor is configured to: determine a property of a predetermined property item from at least one image; generate a plurality of character strings related to the at least one image from the at least one image; and derive a recommended score indicating a degree of recommendation for describing the character string in a document for each of the plurality of character strings based on a result of the determination.

Abstract: A learning support device 18 includes an acquisition unit 26, a registration unit 27, a storage device 28, a learning unit 29, and a controller 31. The acquisition unit 26 acquires an image of a region of interest and a name of the region of interest by analyzing an interpretation report 23. The registration unit 27 registers training data consisting of the image of the region of interest and the name of the region of interest acquired by the acquisition unit 26 in the storage device 28. The learning unit 29 performs learning for generating a discrimination model 34, which outputs the image of the region of interest and the name of the region of interest with respect to an input of an inspection image 22 of the interpretation report 23, using a plurality of pieces of training data 33 registered in the storage device 28.

Abstract: A document creation support apparatus comprising at least one processor, wherein the processor is configured to: acquire an image and a character string related to the image; extract at least one feature region included in the image; specify a specific region that is a region corresponding to a phrase included in the character string, in the feature region; and present information for supporting creation of a document including the character string based on a result of the specifying.

Abstract: Provided are an image processing apparatus, an image processing method, and a program that can collect high-quality correct answer data used for machine learning with a simple method. The image processing apparatus includes: a first extractor that extracts a measurement target region from a medical image, using a result of learning performed using correct answer data of the measurement target region; a measurement object determination unit that determines a measurement object used to measure the measurement target region; a measurement object correction unit that corrects the measurement object in response to a command from a user; and a measurement target region correction unit that corrects the measurement target region extracted by the first extractor, using a correction result of the measurement object. The first extractor performs learning using the measurement target region corrected by the measurement target region correction unit as correct answer data.

Abstract: Provided are a linear structure extraction device, a method, a program, and a learned model which can detect a linear structure in an image. A linear structure extraction device according to an embodiment of the present disclosure includes a learning model that is learned to receive an input of the image and output, as a prediction result, one or more element points which constitute the linear structure from the image, in which the learning model includes a first processing module that receives the image and generates a feature map representing a feature amount of the image by convolution processing, and a second processing module that calculates a shift amount from a unit center point to the element point of the linear structure closest to the unit center point, for each unit obtained by dividing the feature map into a plurality of the units including regions having a predetermined size in a grid pattern.

Abstract: A region specification apparatus specifies a region of an object which is included in an input image and which includes a plurality of subclass objects having different properties. The region specification apparatus includes a first discriminator that specifies an object candidate included in the input image. The first discriminator has a component configured to predict at least one of movement or transformation of a plurality of anchors according to the property of the subclass object and specify an object candidate region surrounding the object candidate.

Abstract: A region specification apparatus, a region specification method, and a region specification program efficiently specify any object included in an input image. A convolutional neural network of an object specification unit generates a convolutional feature map from an input image (S0). A first discriminator selects an anchor based on a similarity in shape and size to a ground truth box including an object candidate from among a plurality of anchors having various shapes and various sizes. The first discriminator specifies an object candidate region in the input image based on the selected anchor.

Abstract: A learning support device 18 includes an acquisition unit 26, a registration unit 27, a storage device 28, a learning unit 29, and a controller 31. The acquisition unit 26 acquires an image of a region of interest and a name of the region of interest by analyzing an interpretation report 23. The registration unit 27 registers training data consisting of the image of the region of interest and the name of the region of interest acquired by the acquisition unit 26 in the storage device 28. The learning unit 29 performs learning for generating a discrimination model 34, which outputs the image of the region of interest and the name of the region of interest with respect to an input of an inspection image 22 of the interpretation report 23, using a plurality of pieces of training data 33 registered in the storage device 28.

Abstract: Provided are a medical image processing apparatus, method, program, and a diagnosis support apparatus, method, and program that can efficiently collect medical images that have a large contribution to improving the accuracy of a diagnosis using medical images. The medical image processing apparatus includes a reception unit that receives an input of a medical image and patient information corresponding to the medical image, an analysis result acquisition unit that acquires an analysis result obtained by analyzing the medical image, a detection unit that detects whether or not the analysis result has been corrected, and a data processing unit that creates and stores data in which identification information capable of identifying a patient is concealed in a case where it is detected that the analysis result has been corrected.

Abstract: Provided are an image processing apparatus, an image processing method, and a program that can collect high-quality correct answer data used for machine learning with a simple method. The image processing apparatus includes: a first extractor that extracts a measurement target region from a medical image, using a result of learning performed using correct answer data of the measurement target region; a measurement object determination unit that determines a measurement object used to measure the measurement target region; a measurement object correction unit that corrects the measurement object in response to a command from a user; and a measurement target region correction unit that corrects the measurement target region extracted by the first extractor, using a correction result of the measurement object. The first extractor performs learning using the measurement target region corrected by the measurement target region correction unit as correct answer data.

Abstract: Provided are a learning system, a method, and a program that can provide medical institutions with motivation for creating learning data related to medical images in a case of using machine learning to support diagnosis using medical images. The learning system includes a reception unit that receives an input of first learning data from a user, a calculation unit that calculates, a contribution degree of the first learning data to learning of a discriminator for each user on the basis of at least one of a comparison result between the first learning data and second learning data used for creating the discriminator or a comparison result between an output obtained by inputting the first learning data to the discriminator and correct answer data corresponding to the first learning data, and a service setting unit that sets a service for the user on the basis of the contribution degree calculated for each user.

Abstract: A virtual conference in relation to diagnostic object data is created. Messages posted to the virtual conference from terminal devices of users participating in the virtual conference are received, and the posted messages are sent to the other terminal devices beside the terminal devices of the users. Information relating to the diagnostic object data is generated, which information is obtained in accordance with analysis results of analyzing the posted messages. The information relating to the diagnostic object data is sent to the terminal devices.

Abstract: Provided are an image processing apparatus, an image processing method, and a program that can register a plurality of medical images with high accuracy. The image processing apparatus includes: an image acquisition unit that acquires a first medical image and a second medical image; an extraction unit that extracts a plurality of regions including a region of interest from each of the first medical image and the second medical image; a landmark region selection unit that selects a specific region which is a region common to the first medical image and the second medical image and is different from the region of interest as a landmark region; and a registration unit that performs rigid registration or linear registration, using the landmark region as a standard for registration, to generate a resultant image in which the first medical image and the second medical image have been superimposed.