An automatic abnormal cell recognition method, the method including: 1) scanning a slide using a digital pathological scanner and obtaining a cytological slide image; 2) obtaining a set of centroid coordinates of all nuclei that is denoted as CentroidOfNucleus by automatically localizing nuclei of all cells in the cytological slide image using a feature fusion based localizing method; 3) obtaining a set of cell square region of interest (ROI) images that are denoted as ROI_images; 4) grouping all cell images in the ROI_images into different groups based on sampling without replacement, where each group contains ROW×COLUMN cell images with preset ROW and COLUMN parameters; obtaining a set of splice images; and 5) classifying all cell images in the splice image simultaneously by using the splice image as an input of a trained deep neural network; and recognizing cells classified as abnormal categories.

Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to identify a region of interest of the target specimen and determine an expression level of, category of, and/or presence of a biomarker in the region of interest, the biomarker comprising at least one from among an epithelial growth factor receptor (EGFR) biomarker and/or a DNA mismatch repair (MMR) deficiency biomarker, the machine learning system having been generated by processing a plurality of training images to predict whether a region of interest is present in the target electronic image, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the determined expression level of, category of, and/or presence of the biomarker in the region of interest.

A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

Systems and methods for automated stereology using deep learning are disclosed. The systems include an update in the form of a semi-automatic approach for ground truth preparation in 3D stacks of microscopy images (disector stacks) for generating more training data. The systems also present an exemplary disector-based MIMO framework where all the planes of a 3D disector stack are analyzed as opposed to a single focus-stacked image (EDF image) per stack. The MIMO approach avoids the costly computations of 3D deep learning-based methods by using the 3D context of cells in disector stacks; and prevents stereological bias in the previous EDF-based method due to counting profiles rather than cells and under-counting overlap-ping/occluded cells. Taken together, these improvements support the view that AI-based automatic deep learning methods can accelerate the efficiency of unbiased stereology cell counts without a loss of accuracy or precision as compared to conventional manual stereology.

Embodiments of the present disclosure include systems, methods, and non-transitory computer-readable storage media for automated determination of a patient tumor-immune phenotype from immunofluorescence (IF) image analysis of pathology slides based on a tumor infiltrating lymphocytes score, a non-tumor infiltrating lymphocytes score, and a non-tumor infiltrating lymphocytes at tumor margin score.

Aspects of the present disclosure relate to a method of processing an input image of a culture device for a condition. The method can include receiving the input image and classifying the input image with a trained machine learning model that is configured to be trained on a training set of images having the condition. The method can include determining that the condition exists in the input image based on the classification and performing at least one action in response to the determination that the condition exists.

An information processing device detects a cell candidate region for determining a unity of a cell from a vessel image obtained by imaging a vessel in which the cell is seeded and includes at least one processor. The processor performs an acquisition process of acquiring the vessel image, performs a detection process of detecting a cell region including the cell and a cell-like region including an object similar to the cell as the cell candidate regions from the acquired vessel image, and an output process of outputting information indicating the detected cell candidate regions.

An object classification system for classifying objects is described. The system comprises an imaging region adapted for irradiating an object of interest, an arrayed detector, and a mixing unit configured for mixing the irradiation stemming from the object of interest by reflecting or scattering on average at least three times the irradiation after its interaction with the object of interest and prior to said detection.

A cell image processing system includes a cell image processing device, a display terminal, and an input unit. The cell image processing device includes an acquisition unit that acquires cell image related data, a storage unit, a data tree creating unit that creates a data tree, and a control unit that performs control to popup display the cell image related data selected by first selection by the input unit in the data tree.

Convolutional neural networks for detecting objects of interest within images of biological specimens are disclosed. Also disclosed are systems and methods of training and using such networks, one method including: obtaining a sample image and at least one of a set of positive points and a set of negative points, wherein each positive point identifies a location of one object of interest within the sample image, and each negative point identifies a location of one object of no-interest within the sample image; obtaining one or more predefined characteristics of objects of interest and/or objects of no-interest, and based on the predefined characteristics, generating a boundary map comprising a positive area around each positive point the set of positive points, and/or a negative area around each negative point in the set of negative points; and training the convolutional neural network using the sample image and the boundary map.