Improving utility of brain tumor confocal laser endomicroscopy: objective value assessment and diagnostic frame detection with convolutional neural networks
Arizona State University（亚利桑那州立大学）；Irkutsk State Medical University（伊尔库茨克国立医科大学）
Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Nikolay Martirosyan, Jennifer Eschbacher, Peter Nakaji, Yezhou Yang, Mark C. Preul
备注：
SPIE Medical Imaging: Computer-Aided Diagnosis 2017
Confocal laser endomicroscopy (CLE), although capable of obtaining images at cellular resolution during surgery of brain tumors in real time, creates as many non-diagnostic as diagnostic images. Non-useful images are often distorted due to relative motion between probe and brain or blood artifacts. Many images, however, simply lack diagnostic features immediately informative to the physician. Examining all the hundreds or thousands of images from a single case to discriminate diagnostic images from nondiagnostic ones can be tedious. Providing a real-time diagnostic value assessment of images (fast enough to be used during the surgical acquisition process and accurate enough for the pathologist to rely on) to automatically detect diagnostic frames would streamline the analysis of images and filter useful images for the pathologist/surgeon. We sought to automatically classify images as diagnostic or non-diagnostic. AlexNet, a deep-learning architecture, was used in a 4-fold cross validation manner. Our dataset includes 16,795 images (8572 nondiagnostic and 8223 diagnostic) from 74 CLE-aided brain tumor surgery patients. The ground truth for all the images is provided by the pathologist. Average model accuracy on test data was 91% overall (90.79 % accuracy, 90.94 % sensitivity and 90.87 % specificity). To evaluate the model reliability we also performed receiver operating characteristic (ROC) analysis yielding 0.958 average for the area under ROC curve (AUC). These results demonstrate that a deeply trained AlexNet network can achieve a model that reliably and quickly recognizes diagnostic CLE images.
Motion Artifact Detection in Confocal Laser Endomicroscopy Images
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Johannes Gutenberg-Universität Mainz（美因茨约翰内斯·古腾堡大学）
Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
Arizona State University（亚利桑那州立大学）；Barrow Neurological Institute（巴罗神经学研究所）；St. Joseph’s Hospital and Medical Center（圣约瑟夫医院与医疗中心）；Irkutsk State Medical University（伊尔库茨克州立医科大学）
Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University Hospital Erlangen（埃尔朗根大学医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Johannes Gutenberg-Universität Mainz（美因茨约翰内斯·古腾堡大学）；University Medical Center Mainz（美因茨大学医学中心）；Universität Regensburg（雷根斯堡大学）
Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images -- A Cross-Site Robustness Assessment
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University Hospital Erlangen（埃尔朗根大学医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Johannes Gutenberg-Universität Mainz（美因茨约翰内斯·古腾堡大学）；University Medical Center Mainz（美因茨大学医学中心）
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；University Medical Center Erlangen（埃尔朗根大学医学中心）；Friedrich-Schiller-Universität Jena（耶拿大学）；University Medical Center Mainz（美因茨大学医学中心）；Johannes Gutenberg-Universität Mainz（美因茨约翰内斯·古腾堡大学）
Explainable Image Classification with Reduced Overconfidence for Tissue Characterisation
The Hamlyn Centre for Robotic Surgery, Imperial College London, UK（帝国理工学院伦敦分校机器人手术中心）；Medical Faculty, University Witten Herdecke（乌尔姆-赫德克大学医学学院）；Medical Faculty, Rheinische Friedrich Wilhelms University of Bonn（波恩莱茵-维尔姆斯大学医学学院）；Department of Neurosurgery, Cologne Medical Center（科隆医疗中心神经外科部门）
Convolutional Neural Networks: Ensemble Modeling, Fine-Tuning and Unsupervised Semantic Localization for Intraoperative CLE Images
Arizona State University（亚利桑那州立大学）；Barrow Neurological Institute（巴罗神经学研究所）；St Joseph’s Hospital and Medical Center（圣约瑟夫医院与医疗中心）；Irkutsk State Medical University（伊尔库茨克国立医科大学）
Deep Learning for Size and Microscope Feature Extraction and Classification in Oral Cancer: Enhanced Convolution Neural Network
Charles Sturt University（查尔斯特大学）；Al Iraqia University（伊拉克大学）；Western Sydney University（西悉尼大学）；Kent Institute Australia（澳大利亚肯特学院）；Asia Pacific International College（亚太国际学院）；Worcester State University（伍斯特州立大学）；University of Kurdistan Hewler（库尔德斯坦休勒大学）；Greater Western Sydney Area Health Services（大西悉尼地区卫生服务局）
