This invention relates to an image processing device (1) comprising an input (2) for receiving image data representative of a region of interest in the body of a patient from a medical X-ray imaging apparatus (100). The image data comprises a first dark-field image obtained for a first X-ray spectrum and a second dark-field image obtained for a second, different, X-ray spectrum. A combination unit (3) provides a combination image that is representative of a medical condition map, e.g. a lung condition map, by combining the first dark-field image and the second dark-field image.

Using a computer-implemented intermediary by which contouring performed by two participants, such as two physicians, can be compared. First, contouring performed by each participant can be compared to contouring performed by the intermediary. Then, by way of the common intermediary and a transitive analysis, contouring performed by each participant can be compared.

This application provides a method for detecting images of testing object using hyperspectral imaging. Firstly, obtaining a hyperspectral imaging information according to a reference image, hereby, obtaining corresponded hyperspectral image from an input image and obtaining corresponded feature values for operating Principal components analysis to simplify feature values. Then, obtaining feature images by Convolution kernel, and then positioning an image of an object under detected by a default box and a boundary box from the feature image. By Comparing with the esophageal cancer sample image, the image of the object under detected is classifying to an esophageal cancer image or a non-esophageal cancer image. Thus, detecting an input image from the image capturing device by the convolutional neural network to judge if the input image is the esophageal cancer image for helping the doctor to interpret the image of the object under detected.

Neural network based systems, methods, and instrumentalities may be used to remove motion artifacts from magnetic resonance (MR) images. Such a neural network based system may be trained to perform the motion artifact removal tasks without reference (e.g., without using paired motion-contaminated and motion-free MR images). Various training techniques are described herein including one that feeds the neural network with pairs of MR images with different levels of motion contamination and forces the neural network learn to correct the motion contamination by transforming a first image of a contaminated pair into a second image of the contaminated pair. Other neural network training techniques are also described with an aim to reduce the reliance on training data that is difficult to obtain.

According to the present application, provided is a computer-implemented method of predicting a clinical activity score for conjunctival hyperemia. The method described in the present application includes: training a conjunctival hyperemia prediction model using a training set; acquiring a first image include at least one eye of a subject and an outer region of an outline of the at least one eye; outputting, by the conjunctival hyperemia prediction model executing on a processor, a first predicted value for a conjunctival hyperemia, a first predicted value for the conjunctival edema, a first predicted value for an eyelid redness, a first predicted value for an eyelid edema, and a first predicted value for a lacrimal edema; and generating a score for the conjunctival hyperemia based on the selected first predicted value for a conjunctival hyperemia.

Systems and methods for vision test and uses thereof are disclosed. A method may be implemented on a mobile device having at least a processor, a camera and a display screen. The method may include capturing at least one image of a user using the camera of the mobile device; interactively guiding the user to a predetermined distance from the display screen of the mobile device based on the at least one image; presenting material on the display screen upon a determination that the user is at the predetermined distance from the display screen; and receiving input from the user in response to the material presented on the display screen. The material presented on the display screen may be for assessing at least one characteristic of the user's vision. Mobile devices and non-transitory machine-readable mediums having machine-executable instructions embodied thereon for assessing a user's vision also are disclosed.

A method for determining a sensitivity distribution of magnetic resonance (MR) receiving coils may include obtaining a reference image of a region of interest (ROI) of a subject. Contrast information between at least two types of tissues of the ROI may be weakened in the reference image. The method may also include determining, based on the reference image, a preliminary radio frequency (RF) field map corresponding to the ROI. The method may also include obtaining a transmitting field map corresponding to the ROI. The method may also include determining, based on the preliminary RF field map and the transmitting map, a sensitivity distribution of MR receiving coils corresponding to the ROI.

In some embodiments, the system is directed to medical assessment software for analyzing one or more medical conditions and enabling communication between a medical professional and a patient. In some embodiments, the system includes one or more graphical user interfaces configured to enable a medical professional to execute one or more of scheduling a virtual appointment, view a virtual schedule, check patients in/out, enter new patients into the system, request patient recorded outcomes, and view patient progress. In some embodiments, the system is configured to implement an artificial intelligence (AI) algorithm configured to identify one or more unique features within the one or more images and use the one or more unique features as one or more fiducials during an analysis of the one or more images. In some embodiments, the analysis includes a determination of whether an abnormal condition associated with an area of skin is progressing toward healing.

Described herein are systems, methods, and programming for analyzing and classifying digital pathology images. Some embodiments include receiving whole slide images (WSIs) and dividing each of the WSIs into tiles. For each WSI, a random subset of the tiles may be selected and augmented views of each of the selected tiles may be generated. For each of the selected tiles, a first convolutional neural network (CNN) may be trained to: generate, using a first one of the augmented views corresponding to the selected tile, a first representation of the selected tile, and predict a second representation of the selected tile to be generated by a second CNN, wherein the second representation is generated based on a second one of the augmented views of the selected tile.

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.