Abstract: A processor is configured to extract a target organ from a medical image, set a plurality of small regions in the target organ, derive a first evaluation value representing a physical quantity of each of the plurality of small regions, derive at least one second evaluation value representing a relationship between the first evaluation values in the plurality of small regions, and derive a third evaluation value indicating presence or absence of an abnormality in the entire target organ based on the second evaluation value.

Abstract: A processor is configured to set a first region including an entire target organ for a medical image, set a plurality of small regions including the target organ in the first region, derive a first evaluation value indicating presence or absence of an abnormality in the first region, derive a second evaluation value indicating the presence or absence of the abnormality in each of the plurality of small regions, and derive a third evaluation value indicating the presence or absence of the abnormality in the medical image from the first evaluation value and the second evaluation value.

Abstract: A discrimination unit discriminates a disease region included in a medical image. The discrimination unit has a first neural network which discriminates a first medical feature in the medical image, and consists of a plurality of processing layers, and a second neural network which discriminates a second medical feature associated with the first medical feature, in the medical image, and consists of a plurality of processing layers. A feature quantity output from the processing layer in the middle of the second neural network or an output result of the second neural network is input to the first neural network.

Abstract: A processor extracts a region of a target organ from a medical image, extracts a region of at least one peripheral organ that is present in a periphery of the target organ from the medical image, derives a positional relationship between the target organ and the peripheral organ, and determines whether or not the target organ is compressed by the peripheral organ based on the positional relationship.

Abstract: Provided are a medical image processing device, an operation method of the medical image processing device, and an operation program of the medical image processing device, which can contribute to find the lesion that is hardly depicted, by using a method based on the thought of the doctor. A CPU includes an image acquisition unit, a first derivation unit, and a second derivation unit. The image acquisition unit acquires a medical image. The first derivation unit analyzes the medical image to derive indirect finding information related to an indirect finding that represents at least one feature of a shape or a property of peripheral tissue of the lesion associated with an occurrence of the lesion. The second derivation unit derives lesion presence probability information indicating a presence probability of the lesion based on the indirect finding information.

Abstract: A medical image processing apparatus includes at least one processor, and the processor acquires a purpose of examination of a target medical image to be interpreted. The processor detects a first abnormal shadow from the target medical image and displays a detection result of the first abnormal shadow on a display. The processor sets, according to the purpose of the examination, a first detection threshold value for detecting the first abnormal shadow or a first display threshold value for displaying the detection result of the first abnormal shadow.

Abstract: An analysis result acquisition unit acquires an analysis result indicating a certainty factor indicating that an abnormality is included in a medical image by analyzing the medical image. A priority deriving unit derives a higher priority as the certainty factor becomes closer to a median value between a maximum value and a minimum value of the certainty factor.

Abstract: The conversion apparatus includes a selection section that selects a category of a term used for a report of a medical image; and a conversion section that converts an input term of the report to the term of the category selected by the selection section, with reference to a conversion table in which terms of plural types of the categories are associated with each other.

Abstract: An analysis result acquisition unit acquires an analysis result of a medical image. A medical information acquisition unit acquires medical information different from the medical image. A priority deriving unit derives a priority of the medical image based on the analysis result and the medical information.

Abstract: An information acquisition unit acquires a learning image including a disease region and a first teacher label that specifies the disease region included in the learning image. A teacher label generation unit generates at least one second teacher label of which a criterion for specifying the disease region is different from the first teacher label. A learning unit trains a discriminator that detects a disease region included in a target image on the basis of the learning image, the first teacher label, and the at least one second teacher label.

Abstract: Provided are a feature quantity calculating method, a feature quantity calculating program, and a feature quantity calculating device which enable calculation of a feature quantity accurately showing chemical properties of a target structure, a screening method, a screening program, and a screening device which enable efficient screening of a pharmaceutical candidate compound using a feature quantity, and a compound creating method, a compound creating program, and a compound creating device which enable efficient creation of a three-dimensional structure of a pharmaceutical candidate compound using a feature quantity. Since the chemical properties of target structures are exhibited as the result of an interaction between the target structure and probes in the periphery thereof, the fact that the degree of accumulation of the probes is similar between target structures indicates that the chemical properties of the target structures are similar.

Abstract: An image acquisition unit acquires a brain image of a subject. A cisternal region extraction unit extracts a cisternal region from the brain image by performing registration between a standard brain image and the brain image. Then, a bleeding region specifying unit specifies a bleeding region based on a first signal value distribution, which is the signal value distribution of the cisternal region extracted from the brain image, and a second signal value distribution, which is the signal value distribution of a cisternal region of the standard brain image.

Abstract: A determination processing unit determines a disease region in a medical image including an axisymmetric structure. A first determination section of the determination processing unit generates a feature amount map of the medical image from the medical image. A second determination section second inverts the feature amount map with reference to a symmetry axis to generate an inverted feature amount map. A third determination section superimposes the feature amount map and the inverted feature amount map on each other and determines the disease region in the medical image using the feature amount map and the inverted feature amount map superimposed on each other.

Abstract: An image acquisition unit acquires a brain image of a subject. A cisternal region extraction unit extracts a cisternal region from the brain image by performing registration between a standard brain image and the brain image. Then, a bleeding region specifying unit specifies a bleeding region based on a first signal value distribution, which is the signal value distribution of the cisternal region extracted from the brain image, and a second signal value distribution, which is the signal value distribution of a cisternal region of the standard brain image.

Abstract: A discrimination unit discriminates a disease region included in a medical image. The discrimination unit has a first neural network which discriminates a first medical feature in the medical image, and consists of a plurality of processing layers, and a second neural network which discriminates a second medical feature associated with the first medical feature, in the medical image, and consists of a plurality of processing layers. A feature quantity output from the processing layer in the middle of the second neural network or an output result of the second neural network is input to the first neural network.

Abstract: A fault diagnosis apparatus acquires, by using an acquisition unit, for each of a plurality of medical devices, installation environment information including a plurality of items about an installation environment in which each of the plurality of medical devices is installed; classifies, by using a classification unit, the plurality of medical devices into a plurality of groups on the basis of the installation environment information; extracts, by using an extraction unit, an item in the installation environment information representing a feature of a group to which a device in which a fault has occurred among the plurality of medical devices belongs, the feature being different from that of the other groups; and performs control, by using a display control unit, to cause a display unit to display an extraction result obtained by the extraction unit.

Abstract: Provided are a feature quantity calculating method, a feature quantity calculating program, and a feature quantity calculating device which enable calculation of a feature quantity accurately showing chemical properties of a target structure, a screening method, a screening program, and a screening device which enable efficient screening of a pharmaceutical candidate compound using a feature quantity, and a compound creating method, a compound creating program, and a compound creating device which enable efficient creation of a three-dimensional structure of a pharmaceutical candidate compound using a feature quantity. Since the chemical properties of the target structures are exhibited as the result of an interaction between the target structure and a probe in the periphery thereof, the fact that the degree of accumulation (feature quantity) of probes is similar between target structures indicates that the chemical properties of the target structures are similar.

Abstract: An analysis result acquisition unit acquires an analysis result of a medical image. A medical information acquisition unit acquires medical information different from the medical image. A priority deriving unit derives a priority of the medical image based on the analysis result and the medical information.

Abstract: An analysis result acquisition unit acquires an analysis result indicating a certainty factor indicating that an abnormality is included in a medical image by analyzing the medical image. A priority deriving unit derives a higher priority as the certainty factor becomes closer to a median value between a maximum value and a minimum value of the certainty factor.

Abstract: A determination processing unit determines a disease region in a medical image including an axisymmetric structure. A first determination section of the determination processing unit generates a feature amount map of the medical image from the medical image. A second determination section second inverts the feature amount map with reference to a symmetry axis to generate an inverted feature amount map. A third determination section superimposes the feature amount map and the inverted feature amount map on each other and determines the disease region in the medical image using the feature amount map and the inverted feature amount map superimposed on each other.