<?xml version='1.0' encoding='UTF-8'?><metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns="http://dublincore.org/documents/dcmi-terms/"><dcterms:title>Ex-vivo Bronchoscopic Images for Visual Navigation</dcterms:title><dcterms:identifier>https://doi.org/10.7910/DVN/8MNI49</dcterms:identifier><dcterms:creator>Banach, Artur</dcterms:creator><dcterms:publisher>Harvard Dataverse</dcterms:publisher><dcterms:issued>2022-11-29</dcterms:issued><dcterms:modified>2023-05-09T18:39:03Z</dcterms:modified><dcterms:description>Ex-vivo training and testing datasets for the 3cGAN bronchoscopic depth estimation model. Details on how the dataset was collected and deployed can be found in the publication.</dcterms:description><dcterms:subject>Engineering</dcterms:subject><dcterms:subject>Medicine, Health and Life Sciences</dcterms:subject><dcterms:isReferencedBy>Banach et al. "Visually Navigated Bronchoscopy using three cycle-Consistent generative adversarial network for depth estimation",  Med. Image Anal. 73, 102164 (2021), https://www.sciencedirect.com/science/article/abs/pii/S1361841521002103</dcterms:isReferencedBy><dcterms:date>2022-11-29</dcterms:date><dcterms:contributor>Banach, Artur</dcterms:contributor><dcterms:dateSubmitted>2022-11-28</dcterms:dateSubmitted><dcterms:license>CC BY-SA 4.0</dcterms:license></metadata>