The present invention provides an apparatus and method for image classification and segmentation based on a feature-guided network, a device, and a medium, and belongs to the technical field of deep learning. A feature-guided classification network and feature-guided segmentation network of the present invention include basic unit blocks. A local feature is enhanced and a global feature is extracted among the basic unit blocks. This resolves a problem that features are not fully utilized in existing image classification and image segmentation network models. In this way, a trained feature-guided classification network and feature-guided segmentation network have better effects and are more robust. The present invention selects the feature-guided classification network or the feature-guided segmentation network based on a requirement of an input image and outputs a corresponding category or segmented image, to resolve a problem that the existing classification or segmentation network model has an unsatisfactory classification or segmentation effect.

The present disclosure provides a method and device for correcting a scanned image, and an image scanning system, and relates to the field of image scanning. The method includes obtaining a scanned image of a scanned object, detecting one or more reference objects from the scanned image, determining a deformation parameter of each reference object of the one or more reference objects based on preset a standard parameter of the each reference object, and correcting the scanned image based on the deformation parameters of the one or more reference objects.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Some embodiments provide a method that predicts data items from a real-world object in real-time. The method captures a video comprising a plurality of frames. The method further performs object detection on a frame in the plurality of frames to determine that the frame includes an object. The method also processes the frame using a plurality of models, wherein each model in the plurality of models is configured to predict a set of candidate data items associated with the object. The method selects one or more candidate data items from the sets of candidate data items associated with the object as a set of data items. The method populates a record with the set of data items.

Example detecting methods and apparatus are described. One example method includes: acquiring a two-dimensional image; and constructing, for each of one or more objects under detection in the two-dimensional image, a structured polygon corresponding to the object under detection based on the acquired two-dimensional image, wherein for each object under detection, a structured polygon corresponding to the object represents projection of a three-dimensional bounding box corresponding to the object in the two-dimensional image; for each object under detection, calculating depth information of vertices in the structured polygon based on height information of the object and height information of vertical sides of the structured polygon corresponding to the object; and determining three-dimensional spatial information of the object under detection based on the depth information of the vertices in the structured polygon and two-dimensional coordinate information of the vertices of the structured polygon in the two-dimensional image.

There is provided a method and a system configured to compensate for image distortions.

An image processing method includes obtaining an original image; partitioning the original image into a first part and a second part such that distortion of at least a part of an image in the first part of the original image is smaller than a predetermined threshold, and distortion of at least a part of an image in the second part of the original image is greater than or equal to the predetermined threshold; correcting the second part of the original image so as to obtain a distortion-corrected image corresponding to the second part; and recognizing the first part of the original image and the distortion-corrected image so as to recognize an object in the original image.

An image processing system receives an image depicting a bundle of boards. The bundle of boards has a front face that is perpendicular to a long axis of boards and the image is captured at an angle relative to the long axis. The image processing system applies a homographic transformation to estimate a frontal view of the front face and identifies a plurality of divisions between rows in the estimate. For each adjacent pair of the plurality of divisions between rows, a plurality of vertical divisions is identified. The image processing system identifies a set of bounding boxes defined by pairs of adjacent divisions between rows and pairs of adjacent vertical divisions. The image processing system may filter and/or merge some bounding boxes to better match the bounding boxes to individual boards. Based on the bounding boxes, the image processing system determines the number of boards in the bundle.