A system and method for reducing the responsibility of the user significantly by applying an optical system that can identify container like sample tubes with respect to their characteristics, e.g., shapes and inner dimensions, from their visual properties by capturing images from a rack comprising container and processing said images for reliably identifying a container tyle.

Condensation associated with the collection and identification of airborne particles is detected. Upon the detection, one or more condensation countermeasures are triggered to address the condensation.

A method and apparatus of a device that classifies an image is described. In an exemplary embodiment, the device segments the image into a region of interest that includes information useful for classification and a background region by applying a first convolutional neural network. In addition, the device tiles the region of interest into a set of tiles. For each tile, the device extracts a feature vector of that tile by applying a second convolutional neural network, where the features of the feature vectors represent local descriptors of the tile. Furthermore, the device processes the extracted feature vectors of the set of tiles to classify the image.

A method for assessing a cancer status of biological tissue includes the steps of: obtaining a Raman spectrum indicating a Raman spectroscopy response of the biological tissue, the Raman spectrum captured using a fiber-optic probe of a fiber-optic Raman spectroscopy system; inputting the Raman spectrum into a boosted tree classification algorithm of a computer program, and using the boosted tree classification algorithm for comparing, in real-time, the captured Raman spectrum to reference data and assessing the cancer status of the biological tissue based on said comparison, the reference data being previously determined based on a set of reference Raman spectra indicating Raman spectroscopy responses of reference biological tissues wherein each of the reference biological tissues is associated with a known cancer status; and generating a real-time output indicating the assessed cancer status of the biological tissue,

The present disclosure relates to method and apparatus for processing cross matching image based on deep learning.

Aspects of the present disclosure include reconfigurable integrated circuits for characterizing particles of a sample in a flow stream. Reconfigurable integrated circuits according to certain embodiments are programmed to calculate parameters of a particle in a flow stream from detected light; compare the calculated parameters of the particle with parameters of one or more particle classifications; classify the particle based on the comparison between the parameters of the particle classifications and the calculated parameters of the particle; and adjust one or more parameters of the particle classifications based on the calculated parameters of the particle. Methods for characterizing particles in a flow stream with the subject integrated circuits are also described. Systems and integrated circuit devices programmed for practicing the subject methods, such as on a flow cytometer, are also provided.

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 for improvements to in-vitro fertilization using morpho-kinetic signatures. These improvements are achieved by analyzing a series of images of a developing embryo (e.g., time-lapse images) as opposed to a single static image. For example, due to the difficulty in identifying clear distinctions between morphological states based on static images, as well as the unpredictability of morpho-kinetic development of an embryo, the system analyzes the development of an embryo as a whole over a given time frame (e.g., fertilization to blastulation), which provides a better prediction of the viability of a given embryo. The analysis may take the form of a morpho-kinetic signature, which itself may be used to determine an optimal time to transfer and/or implant an embryo into a patient.

A cell culture evaluation device includes at least one processor. The processor is configured to acquire a cell image obtained by imaging a cell that is being cultured, to input the cell image to an image machine learning model and output an image feature amount set composed of a plurality of types of image feature amounts related to the cell image from the image machine learning model; and to input the image feature amount set to a data machine learning model and output an expression level set composed of expression levels of a plurality of types of ribonucleic acids of the cell from the data machine learning model.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.