The present disclosure relates to computer-implemented systems and methods for training and using generative adversarial networks to detect abnormalities in images of a human organ. In one implementation, a method is provided for training a neural network system, the method may include applying a perception branch of an object detection network to frames of a first subset of a plurality of videos to produce a first plurality of detections of abnormalities. Further, the method may include using the first plurality of detections and frames from a second subset of the plurality of videos to train a generator network to generate a plurality of artificial representations of polyps, and training an adversarial branch of the discriminator network to differentiate between artificial representations of the abnormalities and true representations of abnormalities. Additionally, the method may include retraining the perception branch based on difference indicators between the artificial representations of abnormalities and true representations of abnormalities included in frames of the second subset of plurality of videos and a second plurality of detections.

An object detection model training method includes: inputting an unannotated first sample image into an initial detection model of a current round, and outputting a first prediction result for a target object, transforming the first sample image and a first prediction position region within the first prediction result to obtain a second sample image and a prediction transformation result in the second sample image; inputting the second sample image into the initial detection model, and outputting a second prediction result for the target object; obtaining a loss value of unsupervised learning according to a difference between the second prediction result and the prediction transformation result; and adjusting model parameters of the initial detection model according to the loss value and returning to the operation of inputting a first sample image into an initial detection model of a current round to perform iterative training, to obtain an object detection model.

Systems and methods are disclosed for identifying images that contain polyps. An exemplary method for identifying images includes: accessing images of a gastrointestinal tract (GIT) captured by a capsule endoscopy device, where: each image of the images is suspected to include a polyp and is associated with a probability of containing the polyp, and the images include seed images, where each seed image is associated with one or more images of the images. The image(s) associated with each seed image is identified as suspected to include the same polyp as the associated seed image. The method includes applying a polyp detection system on the seed images to identify seed images which include polyps, where the polyp detection system is applied to each seed image of based on the image(s) associated with the seed image and the probabilities associated with the seed image and with the associated image(s).

The present disclosure relates to systems and methods for processing real-time video and detecting objects in the video. In one implementation, a system is provided that includes an input port for receiving real-time video obtained from a medical image device, a first bus for transferring the received real-time video, and at least one processor configured to receive the real-time video from the first bus, perform object detection by applying a trained neural network on frames of the received real-time video, and overlay a border indicating a location of at least one detected object in the frames. The system also includes a second bus for receiving the video with the overlaid border, an output port for outputting the video with the overlaid border from the second bus to an external display, and a third bus for directly transmitting the received real-time video to the output port.

The present disclosure relates to techniques for the treatment of patient tissue. The techniques may include resection or removal of such tissue. Error reduction in the identification and presentation of specific tissue types may be realized through these techniques. An image may be received that provides a visual representation of the tissue. Pixels of the image may be segmented to form a boundary. Portions of the image may be classified for resection and resection of the tissue may be performed.

A computer aided assistance system for use in endoscopic colonoscopy procedures. The computer aided assistance system including: at least one videoendoscopic instrument configured to capture image data; a controller comprising hardware, the controller being connected with the at least one videoendoscopic instrument; and a display connected or integral with the controller, wherein the controller being configured to automatically select a treatment guideline based on a combination of both a size and a classification of a lesion shown in the image data and to display the selected treatment guideline on the display.

An endoscope system includes an endoscope and an image processing device attached to one another. The image processing device includes at least one processor configured to perform operations of determining an operator's action based on an action signal from an endoscope inserted into a subject body, deciding whether an image is set as a detection target image based on the operator's action and detecting a specific region from the detection target image. The processor performs an operation of determining whether the operator's action at a time of capturing the image is a treatment action to give the subject body a treatment. Furthermore, the processor detects, from the image, a region, which exhibits a specular reflection and whose time change in area and position is large, as a washed region and then determine the operator's action at the time of capturing the image is the treatment action when the washed region is detected.

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.

Identifying polyps or lesions in a colon. In some variations, computer-implemented methods for polyp detection may be used in conjunction with an endoscope system to analyze the images captured by the endoscopic system, identify any polyps and/or lesions in a visual scene captured by the endoscopic system, and provide an indication to the practitioner that a polyp and/or lesion has been detected.

The present disclosure relates to systems and methods for processing real-time video and detecting objects in the video. In one implementation, a system is provided that includes an input port for receiving real-time video obtained from a medical image device, a first bus for transferring the received real-time video, and at least one processor configured to receive the real-time video from the first bus, perform object detection by applying a trained neural network on frames of the received real-time video, and overlay a border indicating a location of at least one detected object in the frames. The system also includes a second bus for receiving the video with the overlaid border, an output port for outputting the video with the overlaid border from the second bus to an external display, and a third bus for directly transmitting the received real-time video to the output port.