Abstract: There is provided a method, comprising: feeding 2D image(s) of an internal surface of a colon captured by an endoscopic camera, into a machine learning model, wherein the 2D image(s) excludes a depiction of an external measurement tool, wherein the machine learning model is trained on records, each including 2D images of the internal surface of the colon of a respective subject labelled with ground truth labels of respective bounding boxes enclosing respective polyps and at least one of an indication of size and a type of the respective polyp indicating likelihood of developing maligiancy, obtaining a bounding box for a polyp and at least one of an indication of size and type of the polyp, and generating instructions for presenting within the GUI, an overlay of the bounding box over the polyp and the at least one of the indication of size and type of the polyp.

Abstract: There is provided a method of generating instructions for presenting a graphical user interface (GUI) for dynamically tracking at least one polyp in a plurality of endoscopic images of a colon of a patient, comprising: iterating for the plurality of endoscopic images: tracking a location of a region depicting at least one polyp within the respective endoscopic image relative to at least one previous endoscopic image, when the location of the region is external to the respective endoscopic image: computing a vector from within the respective endoscopic image to the location of the region external to the respective endoscopic image, creating an augmented endoscopic image by augmenting the respective endoscopic image with an indication of the vector, and generating instructions for presenting the augmented endoscopic image within the GUI.

Abstract: There is provided a method of generating instructions for presenting a graphical user interface (GUI) for dynamically tracking at least one polyp in a plurality of endoscopic images of a colon of a patient, comprising: iterating for the plurality of endoscopic images: tracking a location of a region depicting at least one polyp within the respective endoscopic image relative to at least one previous endoscopic image, when the location of the region is external to the respective endoscopic image: computing a vector from within the respective endoscopic image to the location of the region external to the respective endoscopic image, creating an augmented endoscopic image by augmenting the respective endoscopic image with an indication of the vector, and generating instructions for presenting the augmented endoscopic image within the GUI.

Abstract: An image processing system connected to an endoscope and processing in real-time endoscopic images to identify suspicious tissues such as polyps or cancer. The system applies preprocessing tools to clean the received images and then applies in parallel a plurality of detectors both conventional detectors and models of supervised machine learning-based detectors. A post processing is also applied in order select the regions which are most probable to be suspicious among the detected regions. Frames identified as showing suspicious tissues can be marked on an output video display. Optionally, the size, type and boundaries of the suspected tissue can also be identified and marked.

Abstract: An image processing system connected to an endoscope and processing in real-time endoscopic images to identify suspicious tissues such as polyps or cancer. The system applies preprocessing tools to clean the received images and then applies in parallel a plurality of detectors both conventional detectors and models of supervised machine learning-based detectors. A post processing is also applied in order select the regions which are most probable to be suspicious among the detected regions. Frames identified as showing suspicious tissues can be marked on an output video display. Optionally, the size, type and boundaries of the suspected tissue can also be identified and marked.

Abstract: An image processing system connected to an endoscope and processing in real-time endoscopic images to identify suspicious tissues such as polyps or cancer. The system applies preprocessing tools to clean the received images and then applies in parallel a plurality of detectors both conventional detectors and models of supervised machine learning-based detectors. A post processing is also applied in order select the regions which are most probable to be suspicious among the detected regions. Frames identified as showing suspicious tissues can be marked on an output video display. Optionally, the size, type and boundaries of the suspected tissue can also be identified and marked.