Provided are a method for providing information about the diagnosis of a gallbladder polyp and a device for providing information about the diagnosis of a gallbladder polyp using same. The method for providing information about the diagnosis of a gallbladder polyp being implemented by a processor includes the steps of receiving an ultrasound medical image including a gallbladder part of a subject, determining the pathogenesis of a gallbladder polyp in the subject using a first assessment model configured to determine the pathogenesis of a gallbladder polyp on the basis of the ultrasound medical image, and determining characteristics of the gallbladder polyp on the basis of a second assessment model configured to classify characteristics of the gallbladder polyp when the gallbladder polyp is determined in the ultrasound medical image.

A parasite analysis system includes a pressure vessel configured to store a biological sample, an imaging cell connected to the pressure vessel, and a waste depository connected to the imaging cell. An input valve controls whether biological sample can flow from the pressure vessel into the imaging cell and an output valve controls whether biological sample can flow from the imaging cell into the waste depository. The parasite analysis system also includes a camera that captures a chronological set of images of a portion of the biological sample in the imaging cell and an image analysis system that analyzes the chronological set of images to generate an estimate of a number of parasites in the portion of the biological sample. Estimates for multiple portions of the biological sample may be generated and sampling techniques used to estimate the number of parasites in the entire biological sample.

A method of visualising the three-dimensional internal surface of a lumen in real-time comprising using various combinations of image, motion and shape data obtained from an endoscope with a trained neural network and a curved lumen model. The three-dimensional internal surface may be unfolded to form a two-dimensional visualisation.

Endoscopic image analysis, endoscopic procedure analysis, and/or component control systems, methods and techniques are disclosed that can analyze images of an endoscopic system and/or affect an endoscopic system to enhance operation, user and patient experience, and usability of image data and other case data.

A method for evaluating a gastrointestinal tract may include characterizing one or more disease parameters using objective measures obtained from imaging data of a gastrointestinal tract. The one or more disease parameters reflect a measure of at least one of lesions, ulcers, bleeding, stenosis, and vasculature. The method may also include using the one or more characterized disease parameters to classify a disease state.

A visualization system including a video processing apparatus (VPA) including a processor; and memory having processing instructions stored therein and executable by the processor, the processing instructions operable to, when executed by the processor: determine an amplification gain level applied by the image sensor; determine, based on the amplification gain level, a denoising level and a corresponding sharpening level; and process image data to denoise and sharpen an image corresponding to the image signals using the denosing level and the sharpening level.

Some aspects of the technology described herein relate to configuring an ultrasound device to perform a three-dimensional ultrasound imaging sweep, and displaying ultrasound images and segmented portions of the ultrasound images as the ultrasound images are collected during the three-dimensional ultrasound imaging sweep. Certain aspects relate to displaying an ultrasound image collected by an ultrasound device and configuring the ultrasound device to perform a three-dimensional ultrasound imaging sweep based on the ultrasound image collected by the ultrasound device.

A problem of imbalanced big data is solved by decoupling a classifier into a neural network for generation of representation vectors and into a classification model for operating on the representation vectors. The neural network and the classification model act as a mapper classifier. The neural network is trained with an unsupervised algorithm and the classification model is trained with a supervised active learning loop. An acquisition function is used in the supervised active learning loop to speed arrival at an accurate classification performance, improving data efficiency. The accuracy of the hybrid classifier is similar to or exceeds the accuracy of comparative classifiers in all aspects. In some embodiments, big data includes an imbalance of more than 10:1 in image classes. The hybrid classifier reduces labor and improves efficiency needed to arrive at an accurate classification performance, and improves recognition of previously-unrecognized images.

An automatic identification method and an identification system for a gastrointestinal marker are provided. The automatic identification method comprises the following steps: determining a suspected gastrointestinal marker region in an image; removing an overlapping suspected gastrointestinal marker region; and determining whether the suspected gastrointestinal marker region is part of the gastrointestinal marker. In the method, by means of processing and analyzing an image, positions of a gastrointestinal marker in the image can be automatically detected.

A platform is provided for generating 3D models of a tumor segmented from a series of 2D medical images and for identifying from these 3D models, radiomic features that may be used for diagnostic, prognostic, and treatment response assessment of the tumor. The radiomic features may be shape-based features, intensity-based features, textural features, and filter-based features. The radiomic features are compared to remove sufficiently redundant features, thereby producing a reduced set of radiomic features, which is then compared to separate genomic data and/or outcome data to identify clinically and biologically significant radiomic features for diagnostic, prognostic, and treatment response assessment, other applications.