Abstract: An image extraction device, an image extraction system, an image extraction method, and an image extraction program are capable of extracting an appropriate reference image from a database by using a pathological image as a query. The image extraction device includes: an acquisition unit which acquires a pathological image; a first calculation unit which calculates a local feature amount of the pathological image; a second calculation unit which calculates a non-local feature amount based on a correlation with the local feature amount; a third calculation unit which calculates a degree of similarity between the non-local feature amount and a feature amount of each of a plurality of reference images stored in a database; and an extraction unit which extracts one or more reference images based on the degree of similarity.

Abstract: An image extraction device, an image extraction system, an image extraction method, and an image extraction program are capable of extracting an appropriate reference image from a database by using a pathological image as a query. The image extraction device includes: an acquisition unit which acquires a pathological image; a first calculation unit which calculates a local feature amount of the pathological image; a second calculation unit which calculates a non-local feature amount based on a correlation with the local feature amount; a third calculation unit which calculates a degree of similarity between the non-local feature amount and a feature amount of each of a plurality of reference images stored in a database; and an extraction unit which extracts one or more reference images based on the degree of similarity.

Abstract: To provide a novel immunotherapeutic agent which can be used for treatment of gastric cancer and the like including diffuse-type gastric carcinoma. An antibody that binds to sulfated glycosaminoglycan and has cell growth inhibitory activity is provided. The antibody of the present invention can be used as a cell growth inhibitor, a targeting reagent for drug delivery, or an agent for detecting cancer.

Abstract: An active metric learning device includes a metric application data analysis unit, a metric optimization unit, and an attribute clustering unit. The metric application data analysis unit is formed with a metric applying module for calculating the distance between data to be analyzed, a data analyzing module for analyzing the data using a predetermined function and the distances between the data to be analyzed and outputting the result of the data analysis, and an analysis result storage unit for storing the result of the data analysis. The metric optimization unit is formed with a feedback converting module for creating side information according to the command of feedback from the user and a metric learning module for generating a metric matrix optimized under a predetermined condition using the created side information. The attribute clustering unit clusters the metric matrix optimized by the metric optimization unit and structuralizes the attributes.

Abstract: An active metric learning device includes a metric application data analysis unit, a metric optimization unit, and an attribute clustering unit. The metric application data analysis unit is formed with a metric applying module for calculating the distance between data to be analyzed, a data analyzing module for analyzing the data using a predetermined function and the distances between the data to be analyzed and outputting the result of the data analysis, and an analysis result storage unit for storing the result of the data analysis. The metric optimization unit is formed with a feedback converting module for creating side information according to the command of feedback from the user and a metric learning module for generating a metric matrix optimized under a predetermined condition using the created side information. The attribute clustering unit clusters the metric matrix optimized by the metric optimization unit and structuralizes the attributes.

Abstract: A metric application unit receives data under analysis having a plurality of attributes and a metric indicative of the distance between the data under analysis, calculates the distance between the data under analysis, and output and stores a data analysis result which is generated from an analysis on the data under analysis with a predetermined function, using the calculated distance between the data under analysis. A metric optimization unit generates side-information based on an indication of feedback information entered from the outside and including either similarities between the data under analysis, or the attributes, or a combination thereof, generates a metric which complies with a predetermined condition, based on the generated side information, and stores the generated metric in a metric learning result storage unit.