Abstract: The present invention provides an image analysis method for generating data indicating a region of a cell nucleus in an image of a tissue or a cell. The image analysis method is a method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, and generates data indicating a region of a cell nucleus in an analysis target image by the deep learning algorithm by generating analysis data from the analysis target image including an analysis target tissue or cell, and inputting the analysis data in the deep learning algorithm.

Abstract: The present invention provides an image analysis method for generating data indicating a region of a cell nucleus in an image of a tissue or a cell. The image analysis method is a method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, and generates data indicating a region of a cell nucleus in an analysis target image by the deep learning algorithm by generating analysis data from the analysis target image including an analysis target tissue or cell, and inputting the analysis data in the deep learning algorithm.

Abstract: An image analysis method for generating data indicating a tumorigenic state of an image of a tissue or a cell. The image analysis method is an image analysis method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, analysis data are generated from the analysis target image including a tissue or cell to be analyzed, the analysis data are input to the deep learning algorithm, and data indicating the tumorigenic state of tissues or cells in the analysis target image are generated by the depth learning algorithm.

Abstract: An image analysis method for analyzing an image of a tissue collected from a subject using a deep learning algorithm of a neural network structure. The image analysis method includes generating analysis data from the analysis target image that includes the tissue to be analyzed, inputting the analysis data to a deep learning algorithm, and generating data indicating a layer structure configuring a tissue in the analysis target image by the deep learning algorithm.

Abstract: The present invention provides an image analysis method for generating data indicating a region of a cell nucleus in an image of a tissue or a cell. The image analysis method is a method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, and generates data indicating a region of a cell nucleus in an analysis target image by the deep learning algorithm by generating analysis data from the analysis target image including an analysis target tissue or cell, and inputting the analysis data in the deep learning algorithm.

Abstract: The present invention provides an image analysis method for generating data indicating a region of a cell nucleus in an image of a tissue or a cell. The image analysis method is a method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, and generates data indicating a region of a cell nucleus in an analysis target image by the deep learning algorithm by generating analysis data from the analysis target image including an analysis target tissue or cell, and inputting the analysis data in the deep learning algorithm.

Abstract: Provided is a cell analyzer including: a light source unit configured to apply light to test cells each containing first substances which are bound to first fluorescent dyes and which serve as an index for therapeutic strategy judgement; an image capturing unit configured to capture an image of fluorescence caused by the light; a processing unit configured to process the image obtained by the image capturing unit; and a display unit configured to display a process result obtained by the processing unit, wherein the processing unit obtains a first image by performing an inactivation process of quenching the first fluorescent dyes, an activation process of activating a part of the first fluorescent dyes that have been quenched, and an image capturing process of capturing, by means of the image capturing unit, an image of the fluorescence by applying light from the light source unit to each test cell; extracts bright points based on the first fluorescent dyes on the basis of the first image; classifies the extra

Abstract: Disclosed is a method for synthesizing cDNA by mixing a target RNA, a first oligonucleotide molecule, a second oligonucleotide molecule, and a reverse transcriptase, using the target RNA as a template, the first oligonucleotide molecule having a first region that hybridizes to the target RNA at 3? end, and having a second region that hybridizes to the second oligonucleotide molecule on 5? side of the first region.

Abstract: An image analysis method for analyzing an image of a tissue collected from a subject using a deep learning algorithm of a neural network structure. The image analysis method includes generating analysis data from the analysis target image that includes the tissue to be analyzed, inputting the analysis data to a deep learning algorithm, and generating data indicating a layer structure configuring a tissue in the analysis target image by the deep learning algorithm.

Abstract: The present invention provides an image analysis method for generating data indicating a region of a cell nucleus in an image of a tissue or a cell. The image analysis method is a method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, and generates data indicating a region of a cell nucleus in an analysis target image by the deep learning algorithm by generating analysis data from the analysis target image including an analysis target tissue or cell, and inputting the analysis data in the deep learning algorithm.

Abstract: An image analysis method for generating data indicating a tumorigenic state of an image of a tissue or a cell. The image analysis method is an image analysis method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, analysis data are generated from the analysis target image including a tissue or cell to be analyzed, the analysis data are input to the deep learning algorithm, and data indicating the tumorigenic state of tissues or cells in the analysis target image are generated by the depth learning algorithm.

Abstract: Provided is a cell analyzer including: a light source unit configured to apply light to test cells each containing first substances which are bound to first fluorescent dyes and which serve as an index for therapeutic strategy judgement; an image capturing unit configured to capture an image of fluorescence caused by the light; a processing unit configured to process the image obtained by the image capturing unit; and a display unit configured to display a process result obtained by the processing unit, wherein the processing unit obtains a first image by performing an inactivation process of quenching the first fluorescent dyes, an activation process of activating a part of the first fluorescent dyes that have been quenched, and an image capturing process of capturing, by means of the image capturing unit, an image of the fluorescence by applying light from the light source unit to each test cell; extracts bright points based on the first fluorescent dyes on the basis of the first image; classifies the extra