Abstract: A focus adjustment method includes: obtaining at least two first microscopic images by performing image capturing of a first subject using a microscope including an objective lens and using a stop brought, the image capturing being performed a plurality of times while changing a position of the objective lens; by inputting the at least two first microscopic images to a learned model, estimating the direction of movement; processing to move the focus relative to the first subject based on the direction of movement; wherein the estimating includes generating a plurality of first partial images; inputting the plurality of first partial images to the learned model to have the learned model estimate a position of the focus of the objective lens; and calculating an estimation position, based on an estimation results on the position of the focus of the objective lens.

Abstract: An image processing method includes a labeled image acquisition step of acquiring a labeled image of biological samples including a plurality of structures that are labeled, a binarized image generation step of binarizing the labeled image to generate binarized images, and a ground-truth image acquisition step of inputting an unknown labeled image into a pre-trained model trained using the labeled image and the binarized images corresponding to the labeled image, thereby acquiring, as a ground-truth image, a binarized image in which the structures in the unknown labeled image appear plausible.

Abstract: Systems and methods are provided to implement classification of objects, based on sensor data regarding the objects, without labels assigned to the sensor data. A system can include one or more processors. The one or more processors can retrieve sensor data regarding an object. The one or more processors can apply the sensor data as input to a classification model to cause the classification model to determine a classification of the object. The classification model can be configured based on training data that includes a plurality of clusters generated by dimensionality reduction of example data regarding example objects. At least one cluster of the plurality of clusters can be associated with the classification. The one or more processors can output the classification of the object.

Abstract: Systems and methods are provided to implement classification of objects, based on sensor data regarding the objects, in a manner that addresses variations in the sensor data, including measurement variables among the objects. A system can include one or more processors to retrieve sensor data regarding an object that is at least one of cellular material from one or more cells, nucleic acid material, biological material, or chemical material. The one or more processors can apply the sensor data as input to a classifier to cause the classifier to determine a classification of the object, the classifier configured based on feature data from a first example of object data and a second example of object data associated with at least one of a different time of detection or a different subject than the first example.

Abstract: An image acquire device comprising: an irradiator configured to irradiate an undyed tissue with excitation light; an image sensor configured to acquire a third harmonic image of the undyed tissue based on light generated in third harmonic generation caused by interaction between the undyed tissue and the excitation light.