Abstract: An image analysis device may obtain target image data representing a target image which is an analysis target, specify (m×n) partial images sequentially by scanning the target image data, wherein the (m×n) partial images are constituted of m partial images aligned along a first direction and n partial images aligned along a second direction, generate first probability data by using the (m×n) partial images and the first object data in the memory, and reduce the target image data so as to generate reduced image data. The image analysis device may execute image analysis according to a convolutional neural network by using the reduced image data as K pieces of channel data corresponding to K channels and using the first probability data as one piece of channel data corresponding to one channel, and output a result of the image analysis.

Abstract: An image analysis device acquires cell image data representing a cell image including a cell object, specifically a plurality of partial images, and determine, for each of the partial images, if this partial image includes the cell object. The device generates first specific data including a plurality of values arranged at a plurality of positions corresponding to the partial images. The values include a presence value arranged at a position corresponding to a partial image that includes the cell object. The device specifies a plurality of partial data from the first specific data, count a number of cell object in the cell image from the plurality of partial data and the plurality of pattern data, and outputs a count result. The image analysis device counts “one” as a number of cell object corresponding to the target partial data despite the partial data including two or more presence values.

Abstract: An image analysis device may include a memory storing learning data for executing image analysis, and may obtain cell image data representing a cell image including a plurality of cell objects, sequentially identify plural pieces of partial image data from the cell image data, sequentially execute a center determination process on each of the plural pieces of partial image data, classify at least one cell corresponding to at least one cell object among a plurality of cell objects by using results of the center determination process on the plural pieces of partial image data and classification data included in learning data, and output a classification result.

Abstract: An image analysis device may obtain target image data representing a target image which is an analysis target, specify (m×n) partial images sequentially by scanning the target image data, wherein the (m×n) partial images are constituted of m partial images aligned along a first direction and n partial images aligned along a second direction, generate first probability data by using the (m×n) partial images and the first object data in the memory, and reduce the target image data so as to generate reduced image data. The image analysis device may execute image analysis according to a convolutional neural network by using the reduced image data as K pieces of channel data corresponding to K channels and using the first probability data as one piece of channel data corresponding to one channel, and output a result of the image analysis.

Abstract: An image analysis device acquires cell image data representing a cell image including a cell object, specifically a plurality of partial images, and determine, for each of the partial images, if this partial image includes the cell object. The device generates first specific data including a plurality of values arranged at a plurality of positions corresponding to the partial images. The values include a presence value arranged at a position corresponding to a partial image that includes the cell object. The device specifies a plurality of partial data from the first specific data, count a number of cell object in the cell image from the plurality of partial data and the plurality of pattern data, and outputs a count result. The image analysis device counts “one” as a number of cell object corresponding to the target partial data despite the partial data including two or more presence values.

Abstract: An image analysis device may include a memory storing learning data for executing image analysis, and may obtain cell image data representing a cell image including a plurality of cell objects, sequentially identify plural pieces of partial image data from the cell image data, sequentially execute a center determination process on each of the plural pieces of partial image data, classify at least one cell corresponding to at least one cell object among a plurality of cell objects by using results of the center determination process on the plural pieces of partial image data and classification data included in learning data, and output a classification result.

Abstract: There is provided a method and an apparatus for collecting a rare gas, which are capable of effectively collecting the rare gas contained in an exhaust gas exhausted from a rare gas using apparatus such as a plasma apparatus or the like, and moreover capable of securely supplying the rare gas having a predetermined purity to the rare gas using apparatus, and the method for collecting rare gas which comprises collecting a rare gas contained in an exhaust gas exhausted from a rare gas using apparatus operated under decompression, the method comprises the step of collecting the rare gas by separating the rare gas and impurities contained in the exhaust gas via at least two gas separating steps.

Abstract: There is provided a method and an apparatus for collecting a rare gas, which are capable of effectively collecting the rare gas contained in an exhaust gas exhausted from a rare gas using apparatus such as a plasma apparatus or the like, and moreover capable of securely supplying the rare gas having a predetermined purity to the rare gas using apparatus, and the method for collecting rare gas which comprises collecting a rare gas contained in an exhaust gas exhausted from a rare gas using apparatus operated under decompression, the method comprises the step of collecting the rare gas by separating the rare gas and impurities contained in the exhaust gas via at least two gas separating