Fujifilm Corporation

Fujifilm Medical Systems U.s.a., Inc.

Akimichi Ichinose

Keigo Nakamura

Yoshiro Kitamura

Noriaki Ida

Keiji Sugihara

Aya Ogasawara

A processor derives a feature amount related to an input image based on first to nth (n≥2) derivation models, derives, via the first derivation model, a first feature amount based on the input image, and derives, via a kth (1<k≤n) derivation model, a kth feature amount based on the input image and a k−1th feature amount derived by a k−1th derivation model. An nth feature amount derived by the nth derivation model is the feature amount related to the input image, and the k−1th feature amount includes a feature amount related to another region that is independent of a region related to the kth feature amount, which suggests the kth feature amount derived by the kth derivation model, and that serves as a basis for the derivation of the kth feature amount.

Image processing apparatus, image processing method, and image processing program

A processor derives a first feature amount for each of a plurality of first objects included in first data by a first neural network, derives a second feature amount for second data including one or more second objects by a second neural network, specifies a first object candidate, which is paired with the second object, from among the plurality of first objects, estimates an attribute of a pair second object, which is paired with the first object candidate, based on the first object candidate, and trains at least one of the first neural network or the second neural network such that a difference between the estimated attribute of the pair second object and an attribute of the pair second object derived from the second data is reduced.

Learning device, learning method, learning program, information processing apparatus, information processing method, and information processing program

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.

Information processing apparatus, information processing method, and information processing program

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.

Information processing apparatus, method, and program

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.

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.

Constructing trained models to associate object in image with description in sentence where feature amount for sentence is derived from structured information

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.

Region specification apparatus, region specification method, region specification program, learning apparatus, learning method, learning program, and discriminator

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.

Document creation support apparatus, document creation support method, and document creation support program

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.

Linear structure extraction device, method, program, and learned model

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.

Medical image processing apparatus, method, and program, and diagnosis support apparatus, method, and program

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.

Diagnostic support server device, terminal device, diagnostic support system, diagnostic support process,diagnostic support device, and diagnostic support program

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.

Learning system, method, and program

A learning support deviceincludes an acquisition unit, a registration unit, a storage device, a learning unit, and a controller. The acquisition unitacquires an image of a region of interest and a name of the region of interest by analyzing an interpretation report. The registration unitregisters training data consisting of the image of the region of interest and the name of the region of interest acquired by the acquisition unitin the storage device. The learning unitperforms learning for generating a discrimination model, 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 imageof the interpretation report, using a plurality of pieces of training dataregistered in the storage device

Learning support device, learning support method, learning support program, region-of-interest discrimination device, region-of-interest discrimination method, region-of-interest discrimination program, and learned model

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.

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Tokyo, Japan

Akimichi Ichinose