The present disclosure relates to computer-implemented systems and methods for detecting a feature-of-interest in a video. In one implementation, a computer-implemented system may include a discriminator network and a generative network. The discriminator network may include a perception branch and an adversarial branch, the perception branch being configured to output detections of the feature-of-interest in the video. The generative network may be configured to receive detections of the feature-of-interest from the perception branch of the discriminator network and generate artificial representations of the feature-of-interest based on the detections from the perception branch. Further, the adversarial branch may be configured to provide an output identifying differences between the false representations and true representations of the feature-of-interest, and the perception branch may be further configured to be trained by the output of the adversarial branch so that false representations are not detected by the perception branch as true representations.

A diagnosis support system includes a processor. The processor is connected to a plurality of classifiers that are different in performance. The processor displays performance information of each of the classifiers side by side, receives a user's selection of the performance information displayed side by side, and inputs an input image to the classifier associated with the performance information selected by the user.

A system includes a processor, a memory configured to store a digital model of the colon and previous anomalies in the digital model based on previous colonoscopy data and to include instructions stored therein. The instructions, when executed by the processor, cause the system to access a frame image captured by a colonoscope, process the frame image to detect current anomalies and debris, display the frame image on a display, display a first shape at positions of the previous anomalies in the frame image based on correlation between the frame image and the digital model, and display a second shape at positions of the current anomalies in the frame image.

A colon polyp image processing method and apparatus and a system are disclosed in the embodiments of this application to detect a position of a polyp in real time and determine a property of the polyp, and thereby improve the processing efficiency of a polyp image. Embodiment of this application provide a colon polyp image processing method that can include detecting a position of a polyp in a to-be-processed endoscopic image by using a polyp positioning model, and positioning a polyp image block in the endoscopic image. The polyp image block can include a position region of the polyp in the endoscopic image. The method can further include performing a polyp type classification type on the polyp image block by using a polyp property identification model, and outputting an identification result.

In an endoscope apparatus including a processor, the processor specifies the position of a specific region and sets a reference scale in a picked-up image that is obtained from the image pickup of a subject on which the specific region formed by auxiliary measurement light is formed. Then, the processor extracts a region of interest, determines a measurement portion, calculates a measured value obtained from the measurement of the measurement portion, on the basis of the reference scale, and generates a measured value marker using the measured value. Further, the processor creates a specific image in which the measured value marker is superimposed on the picked-up image.

An image processing system includes a processor configured to acquire, as a processing target image, an in-vivo image, operate in accordance with a trained model, and output a recognition result representing a result of recognition of a region of interest in the processing target image. The trained model is trained by having undergone pre-training using a first image group including images captured in a first observation method, and having undergone, after the pre-training, fine-tuning that uses a second image group including images captured in a second observation method, as well as that uses ground truth regarding the region of interest included in the second image group. The first observation method is an observation method using normal light as illumination light, and the second observation method is an observation method using special light as the illumination light or an observation method in which a pigment has been dispersed onto the subject.

There is provided an image analysis processing apparatus including a processor, in which the processor acquires a plurality of types of analysis images used in image analysis, performs the image analysis on the analysis image in parallel for each type of analysis image, acquires a plurality of analysis results through the image analysis, and performs control of displaying, on a display, an analysis result display based on the plurality of analysis results and a display image based on at least one type of analysis image among the plurality of types of analysis images.

Generating a structured medical record from endoscopy data includes obtaining image data including endoscopic images representing portions of a gastrointestinal tract (GI) of a patient; determining features to extract from the image data, the features each representing a physical parameter of the GI tract; extracting the features from the image data; generating anatomical location data specifying a location within the GI tract of a portion of the GI tract represented in the image data; associating the anatomical location data with images that represent the portion of the GI tract; storing, in a node of a data store, data entries including the anatomical location data and the associated one or more images. The data store is configured to receive structured queries for the data entries in the data store and provide the data entries including the transformed features in response to receiving the structured queries.

A system for selecting images of an event indicator includes a processor and a memory storing instructions which, when executed, cause the system to: access images of a portion of a gastrointestinal tract captured by a capsule endoscopy device; for each of the images, access one or more scores indicating a presence of an event indicator; select seed images from among the images based on the one or more scores; deduplicate the seed images for images showing the same occurrence of the event indicator, where the deduplicating utilizes a consecutive-image tracker; and present the deduplicated seed images in a graphical user interface to display potential occurrences of the event indicator.

Example embodiments of the present invention relate to an image processing apparatus. The apparatus may include a processor and memory storing instructions that when executed on the processor cause the processor to perform the operations of detecting a deep region of a duct in an image and extracting a plurality of contour edges of an inner wall of the duct in the image. The apparatus then may identify a plurality of convex regions among the plurality of contour edges, analyze a respective curvature of each of the plurality of convex regions to identify a convex direction for each of the plurality of convex regions, and detect, as an abnormal region, a convex region having a convex direction directed toward the deep region.