Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations
University of Las Palmas de Gran Canaria（大加那利岛拉斯帕尔马斯大学）；Imperial College London（伦敦帝国理工学院）；University Hospital Doctor Negrin（内格林大学医院）；University Hospital Southampton（南安普顿大学医院）；University of Cambridge（剑桥大学）；Universidad Politecnica de Madrid（马德里理工大学）
H. Fabelo, S. Ortega, D. Ravi, B. R. Kiran, C. Sosa, D. Bulters, G. M. Callico, H. Bulstrode, A. Szolna, J. F. Pineiro, S. Kabwama, D. Madronal, R. Lazcano, A. … 展开作者
H. Fabelo, S. Ortega, D. Ravi, B. R. Kiran, C. Sosa, D. Bulters, G. M. Callico, H. Bulstrode, A. Szolna, J. F. Pineiro, S. Kabwama, D. Madronal, R. Lazcano, A. J. OShanahan, S. Bisshopp, M. Hernandez, A. Baez-Quevedo, G. Z. Yang, B. Stanciulescu, R. Salvador, E. Juarez, R. Sarmiento
Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a noncontact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.
A self-supervised and adversarial approach to hyperspectral demosaicking and RGB reconstruction in surgical imaging
School of Biomedical Engineering & Imaging Sciences King’s College London London, UK（生物医学工程与成像科学学院 英国伦敦国王学院）；Department of Neurosurgery King’s College Hospital NHS Foundation Trust, London, UK（神经外科部门 英国伦敦国王学院医院 NHS基金会信托）
Semantic segmentation of surgical hyperspectral images under geometric domain shifts
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Heidelberg University Hospital（海德堡大学医院）
Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection
Hamburg University of Technology（汉堡工业大学）；University of Bonn（波恩大学）；Klinikum Braunschweig（布伦瑞克医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks
University of Electronic Science and Technology of China（电子科技大学）；Southwestern University of Finance and Economics（西南财经大学）；University of Salerno（萨勒诺大学）；Institute of Methodologies for Environmental Analysis, CNR-IMAA（意大利国家研究委员会环境分析方法研究所）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立理工学院）
Deep intra-operative illumination calibration of hyperspectral cameras
Siemens AG（西门子股份公司）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Heidelberg University Hospital（海德堡大学医院）；University Medical Center Mannheim（曼海姆大学医学中心）；DKFZ Hector Cancer Institute at the University Medical Center Mannheim（德国癌症研究中心赫克曼海姆大学医学中心癌症研究所）
Spatio-Temporal Representation Decoupling and Enhancement for Federated Instrument Segmentation in Surgical Videos
Department of Electrical and Computer Engineering, NUS, Singapore（新加坡国立大学电子与计算机工程系）；Department of Biomedical Engineering, NUS, Singapore（新加坡国立大学生物医学工程系）；Institute of High Performance Computing, A*STAR, Singapore（新加坡A*STAR高性能计算研究所）；Department of Computer Science and Engineering, The Chinese University of Hong Kong, HKSAR, China（香港中文大学（深圳）计算机科学与工程系）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China（中国科学院深圳先进技术研究所）
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors
University of Electronic Science and Technology of China（电子科技大学）；Oregon State University（俄勒冈州立大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）
mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics
Weldon School of Biomedical Engineering, Purdue University（普渡大学生物医学工程学院）；Regenstrief Center for Healthcare Engineering（里真斯特里夫医疗保健工程中心）；Purdue Quantum Science and Engineering Institute（普渡大学量子科学与工程研究所）
Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging
Carl Zeiss AG（卡尔蔡司股份公司）；University of Padua（帕多瓦大学）；University of Applied Sciences Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州高等专业学院））；Carl Zeiss Meditec AG（卡尔蔡司医疗科技股份公司）；IMEC（比利时微电子研究中心）；University Hospitals Leuven（鲁汶大学医院）；KU Leuven（鲁汶大学）
Surgical Video Motion Magnification with Suppression of Instrument Artefacts
Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London（伦敦大学学院威康/EPSRC介入与外科科学中心）；Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery（国立神经病学与神经外科医院神经外科）
Hyperspectral and multispectral image fusion under spectrally varying spatial blurs -- Application to high dimensional infrared astronomical imaging
University of Toulouse（图卢兹大学）；IRIT/INP-ENSEEIHT（图卢兹计算机研究所/国立图卢兹应用科学学院-国立高等电力技术、电子学、计算机、水力学与电信学院）；CNRS（法国国家科学研究中心）；Institut Universitaire de France (IUF)（法国大学研究院）；ISAE-SUPAERO（法国高等航空航天学院）；Institut de Recherche en Astrophysique et Planétologie (IRAP)（天体物理与行星学研究所）
ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgery
NUS Graduate School for Integrative Sciences and Engineering (NGS), National University of Singapore（新加坡国立大学综合科学与工程研究生院）；Dept. of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；Dept. of Instrumentation and Control Engineering, NIT Trichy（印度国立理工学院蒂鲁奇拉帕利分校仪器与控制工程系）；Dept. of Otolaryngology, Singapore General Hospital（新加坡中央医院耳鼻喉科）
Robust deep learning-based semantic organ segmentation in hyperspectral images
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；Heidelberg University Hospital（海德堡大学医院）；Heidelberg University（海德堡大学）
Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-Resolution
Institute of Applied Computing and Community Code (IAC3)（应用计算与社区代码研究所）；Dept. of Mathematics and Computer Science, Universitat de les Illes Balears（数学与计算机科学系，巴利阿里大学）
Spatio-Temporal Hybrid Fusion of CAE and SWIn Transformers for Lung Cancer Malignancy Prediction
Concordia University（康考迪亚大学）；Concordia Institute for Information Systems Engineering（康考迪亚信息系统工程研究所）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；University of Toronto（多伦多大学）
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution
University of Georgia（佐治亚大学）；Stanford University（斯坦福大学）；Alphabet Inc.（Alphabet公司）；University of British Columbia（不列颠哥伦比亚大学）；University of Connecticut（康涅狄格大学）
