Abstract: The present disclosure relates to a movable contact bracket assembly and a contactor, the movable contact bracket assembly includes a movable contact and a movable contact bracket, the movable contact bracket assembly further includes a magnetic conductive frame, the magnetic conductive frame is configured to at least partially surround the movable contact to protect the movable contact from arc erosion.

Abstract: There is provided a pharmaceutical support device including a processor. The processor is configured to: acquire protein information related to a protein included in a biopharmaceutical to be preserved, prescription information related to a prescription of a candidate preservation solution, which is a candidate for a preservation solution for the biopharmaceutical to be preserved, and measurement data indicating preservation stability of the candidate preservation solution measured by an actual test; derive a feature amount related to the preservation stability of the candidate preservation solution on the basis of the protein information and the prescription information; and input the measurement data and the feature amount to a machine learning model such that a score indicating the preservation stability of the candidate preservation solution is output from the machine learning model.

Abstract: A processor is configured to: acquire protein information related to a protein included in a biopharmaceutical to be preserved and a plurality of prescription information items respectively related to prescriptions of a plurality of types of candidate preservation solutions; derive a plurality of feature amounts respectively related to preservation stability of the plurality of types of candidate preservation solutions; input the feature amount to a first machine learning model such that a first score indicating the preservation stability of the candidate preservation solution is output from the first machine learning model; acquire measurement data indicating preservation stability for a selected candidate preservation solution; and input the measurement data and a feature amount of the selected candidate preservation solution to a second machine learning model such that a second score indicating preservation stability of the selected candidate preservation solution is output from the second machine learning

Abstract: There is provided an information processing apparatus including: a processor; and a memory connected to or built in the processor, in which the processor is configured to generate a scatter diagram for a machine learning model that receives a plurality of types of input data and outputs output data according to the input data, by plotting, in a two-dimensional space in which two parameters which are set based on the plurality of types of input data are set as a horizontal axis and a vertical axis, marks representing a plurality of samples obtained by inputting the input data to the machine learning model, and display the scatter diagram, the input data, and a type of the output data on a display.

Abstract: There is provided a diagnosis support device including a processor and a memory connected to or built in the processor, in which the processor is configured to: acquire a medical image; extract a plurality of anatomical regions of an organ from the medical image; input images of the plurality of anatomical regions to a plurality of feature amount derivation models prepared for each of the plurality of anatomical regions, and output a plurality of feature amounts for each of the plurality of anatomical regions from the feature amount derivation models; input the plurality of feature amounts which are output for each of the plurality of anatomical regions to a disease opinion derivation model, and output a disease opinion from the disease opinion derivation model; and present the opinion.

Abstract: A processor is configured to: acquire a medical image; extract a plurality of anatomical regions of an organ from the medical image; input images of the plurality of anatomical regions to a plurality of feature amount derivation models prepared for each of the plurality of anatomical regions and output a plurality of feature amounts for each of the plurality of anatomical regions from the feature amount derivation models; input the plurality of feature amounts which are output for each of the plurality of anatomical regions to a disease opinion derivation model and output a disease opinion from the disease opinion derivation model; derive a first contribution which represents a degree of contribution to output of the opinion for each of the anatomical regions; and present the opinion and a derivation result of the first contribution for each of the anatomical regions.

Abstract: A diagnosis support device includes a processor and a memory connected to or built in the processor. The processor is configured to perform non-linear registration processing of a target image which is a medical image to be analyzed and at least one representative image generated from a plurality of medical images, input at least one of transformation amount information of the target image obtained by the non-linear registration processing or a feature amount derived from the transformation amount information to a disease opinion derivation model, and output a disease opinion from the disease opinion derivation model.

Abstract: Extraction means analyzes a character string of an interpretation report to extract lesion portion information recorded in the interpretation report and a first lesion feature recorded in the interpretation report, and analysis means performs an image analysis process corresponding to the lesion portion information with respect to the image data corresponding to the interpretation report to acquire a second lesion feature based on a result of the image analysis process. Determination means determines whether the first lesion feature matches the second lesion feature, and registration means registers the image data corresponding to the interpretation report as learning correct answer data in a case where it is determined that the first lesion feature matches the second lesion feature.

Abstract: An image acquisition unit acquires a plurality of medical images, and a common region determination unit determines common regions commonly present in the plurality of medical images. An enlargement ratio determination unit determines an enlargement ratio, which is for displaying the plurality of common regions in the plurality of medical images with the same size, for each of the plurality of medical images. A display control unit applies the determined enlargement ratio to the plurality of common regions, and displays images of the plurality of common regions after applying the enlargement ratio on a display.

Abstract: Finding acquisition means acquires finding data including disease information regarding a brain disease of a target patient and diseased part information regarding a diseased part where the brain disease has occurred. Similar case search means searches for case data similar to the finding data of the target patient as similar case data. Information output means acquires an appropriate range of vital sign data from the similar case data, and outputs information regarding the goal of vital sign data of the target patient.

Abstract: Extraction means analyzes a character string of an interpretation report to extract lesion portion information recorded in the interpretation report and a first lesion feature recorded in the interpretation report, and analysis means performs an image analysis process corresponding to the lesion portion information with respect to the image data corresponding to the interpretation report to acquire a second lesion feature based on a result of the image analysis process. Determination means determines whether the first lesion feature matches the second lesion feature, and registration means registers the image data corresponding to the interpretation report as learning correct answer data in a case where it is determined that the first lesion feature matches the second lesion feature.

Abstract: An image acquisition unit acquires a plurality of medical images, and a common region determination unit determines common regions commonly present in the plurality of medical images. An enlargement ratio determination unit determines an enlargement ratio, which is for displaying the plurality of common regions in the plurality of medical images with the same size, for each of the plurality of medical images. A display control unit applies the determined enlargement ratio to the plurality of common regions, and displays images of the plurality of common regions after applying the enlargement ratio on a display.

Abstract: A target place is set in an area of a human body structure having a tree structure in a three-dimensional image. The tree structure of the human body structure is extracted. A path from a reference point set on a branch upstream side of the target place on the tree structure to the target place is determined. Priority is set for respective portions of the tree structure of the human body structure outside the path based on a positional relationship between the portions and the target place. An initial image representing only a portion of the path and images for confirmation of each step sequentially representing the respective portions of the human body structure outside the path according to the priority are generated from the three-dimensional image. The initial image is displayed, and then, the generated images for confirmation of each step are displayed.

Abstract: An image acquisition unit acquires a real endoscope image. A first evaluation value acquisition unit acquires a first evaluation value indicating the hole likeness of the insertion destination of an endoscope, and a second evaluation value acquisition unit acquires a second evaluation value indicating the boundary likeness of a plurality of bronchi at the bronchial branch. A determination unit determines whether or not a branching structure is included in the real endoscope image using both the first and second evaluation values. When it is determined that a branching structure is included, a virtual endoscope image generation unit generates a virtual endoscope image, and a display control unit displays the real endoscope image and the virtual endoscope image on a display.

Abstract: A determination unit makes a determination as to whether or not at least either one of at least a portion of an upper end vertebra and at least a portion of a lower end vertebra is included in a first medical image of a subject. If the determination is negative, an image obtaining unit obtains a second medical image that allows recognition of a label of the vertebra of the subject. A labeling unit aligns the first medical image with the second medical image and labels the vertebra included in the first medical image.

Abstract: The image display control device includes a thumbnail image display control unit that displays a thumbnail image of a tomographic image forming a three-dimensional image, a display instruction receiving unit that receives an instruction to display the three-dimensional image, and a tomographic image display control unit that displays a plurality of tomographic images on a display screen which is different from a display screen on which the thumbnail image is displayed such that the tomographic images are sequentially switchable when the instruction to display the three-dimensional image is received. The thumbnail image display control unit newly generates a thumbnail image of the tomographic image displayed immediately before the display of the tomographic image ends, changes the current thumbnail image to the new thumbnail image, and displays the new thumbnail image.

Abstract: A target place is set in an area of a human body structure having a tree structure in a three-dimensional image. The tree structure of the human body structure is extracted. A path from a reference point set on a branch upstream side of the target place on the tree structure to the target place is determined. Priority is set for respective portions of the tree structure of the human body structure outside the path based on a positional relationship between the portions and the target place. An initial image representing only a portion of the path and images for confirmation of each step sequentially representing the respective portions of the human body structure outside the path according to the priority are generated from the three-dimensional image. The initial image is displayed, and then, the generated images for confirmation of each step are displayed.

Abstract: A candidate-region for an object-region is set in an image. In a graph including point-S corresponding the object-region, point-T corresponding to a background-region, a point corresponding to each pixel in the image, S-link connecting each pixel and point-S, T-link connecting each pixel and point-T, and N-link connecting each pair of adjacent pixels, a cost is set for each link, graph-cut is performed. Whether a pixel connected to point-S by a link is present in the graph is judged. If no pixel is present, graph-cut is performed at each stage while costs set for all the S-links connecting each pixel in the candidate-region and point-S are increased stepwise in an increment of a predetermined threshold or less until a pixel connected to point-S by a link appears. Whether each pixel in the image belongs to the object-region or the background-region is determined based on a result of graph-cut performed last.

Abstract: Based on a long diameter of a set specific region, a first region estimated as the specific region and a second region estimated as a background region are set within an input image. Based on a density histogram of each pixel in the first region and a density histogram of each pixel in the second region, a first evaluation value which indicates a likelihood that a density value represents the specific region is calculated for each density value. For each pair of two adjacent pixels within the input image, based on the first evaluation values, a second evaluation value which indicates a likelihood that the two adjacent pixels represent a contour is calculated. Based on the second evaluation value, connection costs between a plurality of nodes respectively representing each pair of the two adjacent pixels are set. The plurality of nodes are connected based on the connection costs.

Abstract: At least one of the common regions in time-sequence images is specified. Then, the time-sequence images are adjusted such that the position of that common region in at least one image among the time-sequence images is adjusted to the position of that common region in a different image. Then, the new adjusted time-sequence images are subjected to video compression processing.