Deep reconstruction-recoding network for unsupervised domain adaptation and multi-center generalization in colonoscopy polyp detection.

Background And Objective: Currently, the best performing methods in colonoscopy polyp detection are primarily based on deep neural networks (DNNs), which are usually trained on large amounts of labeled data. However, different hospitals use different endoscope models and set different imaging parameters, which causes the collected endoscopic images and videos to vary greatly in style. There may be variations in the color space, brightness, contrast, and resolution, and there are also differences between white light endoscopy (WLE) and narrow band image endoscopy (NBIE).

Deep Learning for nasopharyngeal Carcinoma Identification Using Both White Light and Narrow-Band Imaging Endoscopy.

Objectives/hypothesis: To develop a deep-learning-based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow-band imaging (NBI) nasopharyngoscopy images.

Study Design: Retrospective study.

Methods: A total of 4,783 nasopharyngoscopy images (2,898 WLI and 1,885 NBI) of 671 patients were collected and a novel deep convolutional neural network (DCNN) framework was developed named Siamese deep convolutional neural network (S-DCNN), which can simultaneously utilize WLI and NBI images to improve the classification performance.

A real-time system using deep learning to detect and track ureteral orifices during urinary endoscopy.

Background And Objective: To automatically identify and locate various types and states of the ureteral orifice (UO) in real endoscopy scenarios, we developed and verified a real-time computer-aided UO detection and tracking system using an improved real-time deep convolutional neural network and a robust tracking algorithm.

Methods: The single-shot multibox detector (SSD) was refined to perform the detection task. We trained both the SSD and Refined-SSD using 447 resectoscopy images with UO and tested them on 818 ureteroscopy images.