Dignity Health

Arizona State University

Evgenii Belykh

Mark C Preul

Mohammadhassan Izadyyazdanabadi

Yezhou Yang

In accordance with some embodiments of the disclosed subject matter, systems, methods, and media for selectively presenting images captured by confocal laser endomicroscopy (CLE) are provided. In some embodiments, a method comprises: receiving images captured by a CLE device during brain surgery; providing the images to a convolution neural network (CNN) trained using at least a plurality of images of brain tissue captured by a CLE device and labeled diagnostic or non-diagnostic; receiving an indication, from the CNN, likelihoods that the images are diagnostic images; determining, based on the likelihoods, which of the images are diagnostic images; and in response to determining that an image is a diagnostic image, causing the image to be presented during the brain surgery.

Systems, methods, and media for selectively presenting images captured by confocal laser endomicroscopy

In accordance with some embodiments of the disclosed subject matter, systems, methods, and media for automatically transforming a digital image into a simulated pathology image are provided. In some embodiments, the method comprises: receiving a content image from an endomicroscopy device; receiving, from a hidden layer of a convolutional neural network (CNN) trained to recognize a multitude of classes of common objects, features indicative of content of the content image; receiving, providing a style reference image to the CNN; receiving, from another hidden layer of the CNN, features indicative of a style of the style reference image; receiving, from the hidden layers of the CNN, features indicative of content and style of a target image; generating a loss value based on the features of the content image, the style reference image, and the target image; minimizing the loss value; and displaying the target image with the minimized loss.

Growing community of inventors

Phoenix, AZ, United States of America

Evgenii Belykh