Learning model generation method, identification method, learning model generation system, identification system, and non-transitory computer-readable storage medium

An identification method of identifying a cell that is a target for identification on the basis of a captured image of the cell, the method comprising:

• acquiring a hologram for identification in which an image of the cell that is a target for identification is captured;
• generating a new hologram for identification having a different focal position from the acquired hologram for identification and increasing a number of holograms for identification;
• and
• identifying a type of cell or cell classification for the cell using a learning model on the basis of at least the acquired hologram and the new hologram.

A learning model generation system configured to generate a learning model for identifying a cell on the basis of a captured image of the cell, the system comprising circuitry configured to:

• acquire a hologram for model generation in which an image of the cell is captured, the hologram for model generation being used to generate the learning model;
• generate a new hologram for model generation having a different focal position from the acquired hologram for model generation and increase a number of holograms for model generation;
• and
• generate the learning model by performing machine learning using at least the acquired hologram and the new hologram as inputs for model generation, wherein the learning model is operative to output a type of cell or cell classification.

An identification system configured to identify a cell that is a target for identification on the basis of a captured image of the cell, the system comprising circuitry configured to:

• acquire a hologram for identification in which an image of the cell that is a target for identification is captured;
• generate a new hologram for identification having a different focal position from the acquired hologram for identification and increase a number of holograms for identification;
• and
• identify a type of cell or cell classification for the cell using a learning model on the basis of at least the acquired hologram and the new hologram.

A non-transitory computer-readable storage medium storing a learning model generation program causing a computer to operate as a learning model generation system configured to generate a learning model for identifying a cell on the basis of a captured image of the cell, the program causing the computer to:

• acquire a hologram for model generation in which an image of the cell is captured, the hologram for model generation being used to generate the learning model;
• generate a new hologram for model generation having a different focal position from the acquired hologram for model generation and increase a number of holograms for model generation;
• and
• generate the learning model by performing machine learning using at least the acquired hologram and the new hologram as inputs for model generation, wherein the learning model is operative to output a type of cell or cell classification.

A non-transitory computer-readable storage medium storing an identification program causing a computer to operate as identification system configured to identify a cell that is a target for identification on the basis of a captured image of the cell, the program causing the computer to:

• acquire a hologram for identification in which an image of the cell that is a target for identification is captured;
• generate a new hologram for identification having a different focal position from the acquired hologram for identification and increase a number of holograms for identification;
• and
• identify a type of cell or cell classification for the cell using a learning model on the basis of at least the acquired hologram and the new hologram.