A high-power-microscope-assisted identification method of maize haploid plants is provided, the method is implemented by a device including a high power microscope, a main frame disposed on an objective table of the high power microscope and a computer and includes four procedures of sample information input, automatic testing of a batch of samples, automatic analysis and comparison, and automatic generation of data results. Vertical sliding grooves are symmetrically formed in the main frame, and a vertical supporting plate is disposed at an upper end of the main frame. Horizontal sliding grooves are symmetrically formed in the vertical supporting plate, and a horizontal supporting plate is disposed on the vertical supporting plate.

Methods and systems are provided to classify or identify a target molecule or its properties from regions of molecular surface of the target molecule. In an implementation, the system identifies patches of the surface, generates a respective latent space ID and a respective real space ID for each of the patches, uses the latent space IDs and the real space IDs to identify at least one candidate item that includes a surface resembling a surface region of the target molecule, wherein the surface region comprises multiple patches in the plurality of surface patches of the target molecule, and uses the at least one candidate item to determine an identification or a classification of the target molecule.

A way to design a codebook for estimating the type of a molecule at a particular location in a fluorescence microscopy image makes use of one or both of (1) knowledge of the non-uniform prior distribution of molecule types (i.e., some types are known a priori to occur more frequently than others) and/or knowledge of co-occurrence of molecule types at close locations (e.g., in a same cell); and (2) knowledge of a model of the (e.g., random) process that yields the intensities that are expected at a location when a molecule with a particular subset of markers (i.e., a molecule of a type that has been assigned a codeword that defines that subset) is present at that location. The codebook design may provide experimental efficiency by reducing the number of images that need to be acquired and/or improve classification or detection accuracy by making the codewords for different molecule types more distinctive.

Methods for classifying fluorescent flow cytometer data are provided. In some instances, methods include processing the flow cytometer data with a supervised algorithm configured to cluster the fluorescent flow cytometer data into distinct populations according to the relationship of data points to relevant threshold values. In embodiments, methods include determining a measure of spillover spreading by calculating spillover spreading coefficients and combining them in a spillover spreading matrix. In some embodiments, populations of fluorescent flow cytometer data are adjusted to subtract the magnitude of spillover spreading. In embodiments, spillover spreading adjusted populations are partitioned after potential partitions are evaluated relative to the threshold values. In embodiments, partitioned populations of fluorescent flow cytometer data are classified (i.e., phenotyped) according to a hierarchy. Systems and computer-readable media for classifying fluorescent flow cytometer data are also provided.

Systems and methods are disclosed for receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of tissue specimen, determining a detection machine learning model, the detection machine learning model being generated by processing a plurality of training images to output a cancer qualification and further a cancer quantification if the cancer qualification is an confirmed cancer qualification, providing the digital image as an input to the detection machine learning model, receiving one of a pathological complete response (pCR) cancer qualification or a confirmed cancer quantification as an output from the detection machine learning model, and outputting the pCR cancer qualification or the confirmed cancer quantification.

Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.

Some embodiments of the methods provided herein relate to sample analysis and particle characterization methods for large, multi-parameter data sets. Frequency difference gating compares at least two different data sets to identify regions in a multivariate space where a frequency of events from a first data set is different than a frequency of events from the second data set according to a defined threshold.

In one embodiment, a method of machine learning and/or image processing for spectral object classification is described. In another embodiment, a device is described for using spectral object classification. Other embodiments are likewise described.

The present invention relates to image data processing, in particular to a method and system for annotation of medical images. The method includes: retrieving a plurality of medical images; preparing reports by physicians based on assessment of the retrieved medical images; sending the initial data that contains images and reports from database to merge selector, wherein the data is prepared and exported to annotation; checking the returned annotated data for discrepancies and disagreements; sending correctly annotated data without discrepancies and disagreements from the database for the input to the model training generating a trained model which makes automatic data annotations, wherein all decisions made by the trained model are checked additionally, and if there are discrepancies, the decisions are corrected and returned to the database for the improvement of the model training.

A measuring device according to the present technology includes a light emitting unit configured to emit light to a fluid, a light receiving unit configured to perform photoelectric conversion for incident light using an electron avalanche phenomenon by a plurality of pixels to obtain a light reception signal, and a control unit configured to perform processing of detecting a target object in the fluid on the basis of the light reception signal and execute an imaging operation of the target object on condition that the target object is detected.