The invention relates to a method for a computer device, for detecting an optimal candidate compound based on a plurality of samples comprising a cell line and one or more biomarkers, and a plate map configuration, wherein the plate map configuration is providing locations of samples comprising cell lines exposed to one or more biomarkers and different concentrations of a candidate compound forming at least one concentration gradient, the candidate compound being comprised in a plurality of candidate compounds, said method comprising generating (310) phenotypic profiles of each concentration gradient of each of the plurality of candidate compounds at a plurality of successive points in time to form a plurality of compound profiles, wherein generating phenotypic profiles comprises the steps obtaining (312) image data depicting each sample comprised in the concentration gradient, generating (314) a class-label and a class for each cell of the samples based on the image data, detecting (320) the optimal candidate compound by evaluating a comparison criterion on the plurality of compound profiles. Furthermore, the invention also relates to corresponding computer device, a computer program, and a computer program product.

In accordance with some embodiments of the disclosed subject matter, systems, methods, and media for selectively presenting images captured by confocal laser endomicroscopy (CLE) are provided. In some embodiments, a method comprises: receiving images captured by a CLE device during brain surgery; providing the images to a convolution neural network (CNN) trained using at least a plurality of images of brain tissue captured by a CLE device and labeled diagnostic or non-diagnostic; receiving an indication, from the CNN, likelihoods that the images are diagnostic images; determining, based on the likelihoods, which of the images are diagnostic images; and in response to determining that an image is a diagnostic image, causing the image to be presented during the brain surgery.

The present disclosure proposes a modeling method and apparatus for diagnosing an ophthalmic disease based on artificial intelligence, and a storage medium. The modeling method includes: establishing a data collection of ophthalmic images and a data collection of non-image ophthalmic disease diagnosis questionnaires; training a first neural network model by employing the data collection of the ophthalmic images to obtain a first classification model; training a second classification model by employing the data collection of non-image ophthalmic disease diagnosis questionnaires; and merging the first classification model and the second classification model to obtain a target classification network model, in which, a test result outputted by the target classification network model is used as a diagnosis result of the ophthalmic disease.

The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be disqualified from further analysis. Relevant patches can analyzed and stored with various feature parameters.

The invention relates to a method for digitally staining a cell and/or a medical preparation, the method comprising the following steps: determining three-dimensional information of a cell and/or of a medical preparation by means of an analyser for analysing a medical sample, the analyser comprising an apparatus for determining the three-dimensional information of the cell and/or of the medical preparation; digitally staining the cell and/or the medical preparation according to a predetermined correlation between the three-dimensional information of the cell and/or of the medical preparation and the staining of a corresponding cell and/or medical preparation and/or cellular and/or sub-cellular structures of the cell and/or of the medical preparation by means of a staining protocol; representing the digitally stained cell and/or the preparation, the representation involving a predetermined defocus region, and regions of the cell and/or of the preparation being represented by means of different digital staining in the area of the defocus region as corresponding modulations of colour intensities and/or as mixed colour.

Methods and systems for quantitation of features of interest (e.g., extrachromosomal DNA) in whole slide images are disclosed herein. One or more methods and systems described herein are used to reduce bias in the quantitation of the features of interest.

A method for classifying and counting objects recoverable from a urine sample processed onto a slide. The method includes the following steps: receiving at least one digitalized image of the whole slide; detecting connected components by segmentation of the image of the whole slide; classifying the detected connected components into countable connected components and uncountable connected components using a classifier; for the countable connected components using an object detection model to obtaining the number of objects for each class; for the uncountable components using a semantic segmentation model to obtaining the number of objects for each class; summing up the number of objects for each class obtained from the semantic segmentation model and the object detection model outputting a number of objects for each class.

To improve a determination accuracy when determining each particle contained in an image of an object. An image analysis apparatus according to an embodiment of the present invention includes: a shape determination unit configured to determine a shape of a particle included in a particle image that is extracted from an image of an object, so that an OK particle image which is a particle image of an OK particle that satisfies a predetermined standard for shape and a provisional NG particle image which is a particle image of a provisional NG particle that does not satisfy the predetermined standard, are obtained; a pseudo image generation unit configured to generate a pseudo image; and a similarity determination unit configured to determine whether the provisional NG image and the pseudo image are similar.

The present application provides methods and systems for detecting and quantifying chromosomal instability from histology images with machine learning. Also described herein are methods for selecting treatments for a medical disease, by determining a chromosomal instability pathological metric from histology images. The disclosed methods and systems may also be used to investigate disease progression and prognosis.

A device for analyzing cell morphology and a method for identifying cells are provided. A digital camera photographs a cell image of a blood sample under a low-magnification objective lens. A processor identifies and positions suspected cells of preset type in the cell image to obtain an identification result. Based on the identification result and a target number, the processor determines a number of suspected cells of preset type to be identified and positioned under the low-magnification objective lens. The digital camera further photographs, under a high-magnification objective lens, the suspected cells of preset type identified and positioned, and then the processor identifies whether the suspected cells of preset type photographed are cells of preset type, to count the number of cells of preset type photographed under the high-magnification objective lens and obtain a statistical value. If the statistical value≥the target number, photographing is stopped.