隐式神经表征（INRs）在场景重建和计算机图形学领域已取得亮眼成果，其性能主要通过重建精度来评估。随着INRs逐步应用于模型预测会支撑高风险决策的其他领域，INR推理的不确定性量化变得至关重要。为此，我们在计算机断层扫描场景下研究了INR的贝叶斯重构方法UncertaINR，并从精度和校准度两方面评估了多种贝叶斯深度学习实现方案。我们发现这些方案既能实现校准良好的不确定性，又能保持与其他经典方法、基于INR的方法以及基于CNN的重建技术相当的精度。与贝叶斯深度学习文献中的普遍认知相反，我们发现INR采用计算高效的蒙特卡洛随机失活（Monte Carlo dropout）能获得最佳校准效果，性能优于哈密顿蒙特卡洛（Hamiltonian Monte Carlo）和深度集成（deep ensembles）方法。此外，与性能最优的现有方法不同，UncertaINR不需要大型训练数据集，仅需少量验证图像即可。
英文摘要：
Implicit neural representations (INRs) have achieved impressive results for scene reconstruction and computer graphics, where their performance has primarily been assessed on reconstruction accuracy. As INRs make their way into other domains, where model predictions inform high-stakes decision-making, uncertainty quantification of INR inference is becoming critical. To that end, we study a Bayesian reformulation of INRs, UncertaINR, in the context of computed tomography, and evaluate several Bayesian deep learning implementations in terms of accuracy and calibration. We find that they achieve well-calibrated uncertainty, while retaining accuracy competitive with other classical, INR-based, and CNN-based reconstruction techniques. Contrary to common intuition in the Bayesian deep learning literature, we find that INRs obtain the best calibration with computationally efficient Monte Carlo dropout, outperforming Hamiltonian Monte Carlo and deep ensembles. Moreover, in contrast to the best-performing prior approaches, UncertaINR does not require a large training dataset, but only a handful of validation images.
A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography
Universidade Federal Fluminense (UFF)（弗鲁米嫩塞联邦大学）；Universidade Federal do Rio de Janeiro (UFRJ)（里约热内卢联邦大学）；Universidade Federal do Espírito Santo (UFES)（圣埃斯皮里图联邦大学）
Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center（西达赛奈医学中心生物医学成像研究所）；Cedars-Sinai Medical Center（西达赛奈医学中心）；Semmelweis University（塞梅尔维斯大学）；Azienda Ospedaliero-Universitaria di Parma（帕尔马大学医院）；Monash Health（莫纳什医疗中心）；Centro Cardiologico Monzino IRCCS, University of Milan（米兰大学蒙齐诺心脏中心IRCCS）；Showa University School of Medicine（昭和大学医学院）
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images
National University of Singapore（新加坡国立大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡全国眼科中心）；Cardiff University（卡迪夫大学）；Newcastle Research and Innovation Institute（纽卡斯尔研究创新研究所）；National University Hospital（国立大学医院）
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography
Moorfields Eye Hospital NHS Foundation Trust（莫菲尔德眼科医院NHS基金会信托）；University College London（伦敦大学学院）；University of Amsterdam（阿姆斯特丹大学）；University of Cologne（科隆大学）；Goethe University（歌德大学）；Amsterdam UMC location University of Amsterdam（阿姆斯特丹大学阿姆斯特丹医学中心）；Radboudumc（拉德堡德大学医学中心）；University of Washington（华盛顿大学）；Centre for Eye Research Australia（澳大利亚眼科研究中心）；Royal Victorian Eye and Ear Hospital（皇家维多利亚眼耳医院）；The University of Melbourne（墨尔本大学）；National Eye Institute (NEI), National Institutes of Health (NIH)（美国国立卫生研究院国家眼科研究所）
Deep learning vessel segmentation and quantification of the foveal avascular zone using commercial and prototype OCT-A platforms
Simon Fraser University（西蒙菲莎大学）；University of British Columbia（不列颠哥伦比亚大学）；University of Western Australia（西澳大学）；Lions Eye Institute（狮子眼科研究所）；Vitreous Retina Macula Consultants of New York（纽约玻璃体视网膜黄斑 Consultants）；Manhattan Eye, Ear and Throat Hospital（曼哈顿眼耳鼻喉医院）；University of Zagreb（萨格勒布大学）
Towards Label-Free 3D Segmentation of Optical Coherence Tomography Images of the Optic Nerve Head Using Deep Learning
Ophthalmic Engineering and Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore（新加坡国立大学工程学院生物医学工程系眼科工程与创新实验室）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡国立眼科中心新加坡眼科研究所）；Rajan Eye Care Hospital（拉詹眼科护理医院）
Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging
Department of Electrical and Computer Engineering, University of Rochester（电气与计算机工程系，罗切斯特大学）；Goergen Institute for Data Science & AI, University of Rochester（数据科学与人工智能研究院，罗切斯特大学）
Multi-view polarimetric scattering cloud tomography and retrieval of droplet size
Viterbi Faculty of Electrical Engineering（维特比电气工程学院）；Technion - Israel Institute of Technology（以色列理工学院）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；University of Illinois（伊利诺伊大学）
Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography
Michigan State University（密歇根州立大学）；Volkswagen AG（大众汽车集团）；Max-Planck-Institut für extraterrestrische Physik（马克斯·普朗克地外物理研究所）；Eötvös Loránd University（厄特沃什·罗兰大学）
Inter-vendor harmonization of Computed Tomography (CT) reconstruction kernels using unpaired image translation
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）
Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries
Case Western Reserve University（凯斯西储大学）；University Hospitals Cleveland Medical Center（克利夫兰大学医院医疗中心）；The Hashemite University（哈希姆大学）；University of South Florida（南佛罗里达大学）
LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification
Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）；Department of Biostatistics & Bioinformatics, Duke University（杜克大学生物统计与生物信息学系）；Department of Radiology, Duke University（杜克大学放射学系）；Department of Computer Science, Duke University（杜克大学计算机科学系）；Department of Surgery, Duke University（杜克大学外科系）
Robust Detection of Retinal Neovascularization in Widefield Optical Coherence Tomography
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学凯西眼科研究所）；Department of Biomedical Engineering, Oregon Health & Science University（俄勒冈健康与科学大学生物医学工程系）；Department of Ophthalmology, Mackay Memorial Hospital（马偕纪念医院眼科）；Department of Ophthalmology, Aichi Medical University（爱知医科大学眼科）
Artificial Intelligence-assisted Pixel-level Lung (APL) Scoring for Fast and Accurate Quantification in Ultra-short Echo-time MRI
Australian E-health Research Centre, CSIRO（澳大利亚电子健康研究中心，联邦科学与工业研究组织）；The University of Queensland（昆士兰大学）；Queensland Children's Hospital（昆士兰儿童医院）；Siemens Healthcare Pty Ltd（西门子医疗有限公司）；Siemens Healthineers AG（西门子医疗股份公司）
Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography
School of Computer Science and Technology, Zhengzhou University of Light Industry（郑州轻工业大学计算机与通信工程学院）；School of Information Management and Engineering, Shanghai University of Finance and Economics（上海财经大学信息管理与工程学院）；Department of Applied Computing, Michigan Technological University（密歇根理工大学应用计算系）；Health Research Institute, Michigan Technological University（密歇根理工大学健康研究所）；Department of Computer Science, Kennesaw State University（肯尼索州立大学计算机科学系）
A practical artificial intelligence framework for legal age estimation using clavicle computed tomography scans
Panacea Cooperative Research S.Coop.；Department of Computer Science and Artificial Intelligence, University of Granada（计算机科学与人工智能系，格拉纳达大学）；Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI Institute)（数据科学与计算智能安达卢西亚研究所（DaSCI研究所））；Department of Organisms and Systems Biology, Faculty of Biology, University of Oviedo（生物系，奥维耶多大学 organisms and Systems Biology 系）；Faculty of Computer Science, CITIC, University of A Coruña（计算机科学系，阿拉斯加州立大学）
Demonstration of machine-learning-enhanced Bayesian quantum state estimation
University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Arizona State University（亚利桑那州立大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；DEVCOM US Army Research Laboratory（美国陆军作战能力发展司令部陆军研究实验室）；Tulane University（杜兰大学）
Comp2Comp: Open-Source Software with FDA-Cleared Artificial Intelligence Algorithms for Computed Tomography Image Analysis
Stanford Mussallem Center for Biodesign（斯坦福穆萨勒姆生物设计中心）；Stanford Center for Artificial Intelligence in Medicine and Imaging（斯坦福医学与影像人工智能中心）；Stanford Department of Radiology（斯坦福放射科）；Stanford Division of Vascular Surgery, Department of Surgery（斯坦福血管外科 division，外科部）；University of Mississippi Medical Center Department of Data Science（密苏里大学医学中心数据科学部）
Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification
Georgia Institute of Technology（佐治亚理工学院）；Rice University（莱斯大学）；University Medical Center Utrecht（乌得勒支大学医学中心）；Centrum Wiskunde & Informatica（荷兰数学与计算机科学研究院）
GS-DOT: Gaussian splatting-based image reconstruction for diffuse optical tomography
Computational Optical Imaging Group, Biomedical Optics Research Laboratory, Dept. of Neonatology, University Hospital Zurich（计算光学成像组、生物医学光学研究实验室、新生儿科部门、苏黎世大学医院）；University of Zurich, Switzerland（苏黎世大学，瑞士）
Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images
Imsight AI Research Lab（Imsight人工智能研究实验室）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology（香港科技大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images
Guangdong University of Technology（广东工业大学）；Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences)（广东省人民医院（广东省医学科学院））；Southern Medical University（南方医科大学）；Missouri University of Science and Technology（密苏里科技大学）
