Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening
Scientific Computing and Imaging Institute, School of Computing, University of Utah（犹他大学计算学院科学计算与成像研究所）；Department Of Biochemistry, University of Utah（犹他大学生物化学系）
Hong Xu, David E. Timm, Shireen Y. Elhabian
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Accepted for publication in MICCAI 2020, the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention
Cryogenic electron microscopy (cryo-EM) has become an enabling technology in drug discovery and in understanding molecular bases of disease by producing near-atomic resolution (less than 0.4 nm) 3D reconstructions of biological macromolecules. The imaging process required for 3D reconstructions involves a highly iterative and empirical screening process, starting with the acquisition of low magnification images of the cryo-EM grids. These images are inspected for squares that are likely to contain useful molecular signals. Potentially useful squares within the grid are then imaged at progressively higher magnifications, with the goal of identifying sub-micron areas within circular holes (bounded by the squares) for imaging at high magnification. This arduous, multi-step data acquisition process represents a bottleneck for obtaining a high throughput data collection. Here, we focus on automating the early decision making for the microscope operator, scoring low magnification images of squares, and proposing the first deep learning framework, XCryoNet, for automated cryo-EM grid screening. XCryoNet is a semi-supervised, attention-guided deep learning approach that provides explainable scoring of automatically extracted square images using limited amounts of labeled data. Results show up to 8% and 37% improvements over a fully supervised and a no-attention solution, respectively, when labeled data is scarce.
Unsupervised single-particle deep clustering via statistical manifold learning
Peking University（北京大学）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Intel Corporation（英特尔公司）；Peking University Health Science Center（北京大学医学部）；Harvard Medical School（哈佛医学院）
Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings
ETH Zurich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）；University of Regensburg（莱茵河畔埃尔兰根大学）；University Children’s Hospital（儿童医院）；University Children’s Hospital Regensburg（埃尔兰根儿童医院）
Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching
Carnegie Mellon University（卡内基梅隆大学）；University of Florida（佛罗里达大学）
Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records
Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医学中心）；New Hampshire Colonoscopy Registry（新罕布什尔结肠镜检查登记处）；White River Junction VA Medical Center（怀特河交界处退伍军人事务医疗中心）；Dartmouth College（达特茅斯学院）
Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening
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）；ACRV, The Australian National University（澳大利亚机器人视觉中心，澳大利亚国立大学）；University of California, Berkeley（加州大学伯克利分校）
A Lightweight Face Quality Assessment Framework to Improve Face Verification Performance in Real-Time Screening Applications
theCircle Ltd（theCircle有限公司）；General Department of Forensic Science and Criminology Dubai Police Headquarters（迪拜警察总部法医学与犯罪学部门）；Centre for Visual Computing and Intelligent Systems（视觉计算与智能系统中心）
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
KTH Royal Institute of Technology（皇家理工学院）；SciLifeLab；Karolinska Institutet（卡罗林斯卡研究所）；Karolinska University Hospital（卡罗林斯卡大学医院）；Saint Göran Hospital（圣戈兰医院）
MobileCaps: A Lightweight Model for Screening and Severity Analysis of COVID-19 Chest X-Ray Images
National Institute of Technology Karnataka（卡纳塔克邦国立理工学院）；Kasturba Medical College and Hospital, Manipal（马尼帕尔卡斯图巴医学院及医院）；Manipal Academy of Higher Education（马尼帕尔高等教育学院）；J.S.S. Medical College, Mysore（迈索尔J.S.S.医学院）；JSS Academy of Higher Education and Research（JSS高等教育与研究学院）
Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Department of Vascular Surgery, The First Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第一医院血管外科）；Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院心内科）；Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院放射科）；Department of Vascular Surgery, Quzhou People’s Hospital（衢州人民医院血管外科）；Department of Cardiology, The Second Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第二医院心内科）；China Ship Scientific Research Center（中国船舶科学研究院）；Guangdong Transtek Medical Electronics Co., Ltd.（广东 Transtek 医疗电子有限公司）
Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening
Harvard Medical School（哈佛医学院）；Shenzhen University（深圳大学）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）
The Impact of Longitudinal Mammogram Alignment on Breast Cancer Risk Assessment
Department of Physics and Technology, UiT The Arctic University of Norway（乌伊特极地大学物理与技术系）；SPKI The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway（挪威临床人工智能中心，北挪威大学医院）；Department of Computer Science and Engineering, University of Bologna（博洛尼亚大学计算机科学与工程系）；Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering IMTE（弗劳恩霍夫个性化与细胞基医学工程研究所）；Department of Radiology, Netherlands Cancer Institute (NKI)（荷兰癌症研究所放射科）；Norwegian Computing Center（挪威计算中心）；Pioneer Centre for AI, University of Copenhagen（人工智能先锋中心，哥本哈根大学）
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
Computer Vision Center（计算机视觉中心）；Universitat Autonoma de Barcelona（巴塞罗那自治大学）；Montreal Institute for Learning Algorithms（蒙特利尔学习算法研究所）；Université de Montréal（蒙特利尔大学）；École Polytechnique de Montréal（蒙特利尔理工学院）；Hospital Clinic（临床医院）；Universidad de Barcelona（巴塞罗那大学）；Imagia Inc.（Imagia公司）
Adjustable Robust Transformer for High Myopia Screening in Optical Coherence Tomography
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）
Dual-Resolution Attention-Gated Deep Learning with Ordinal Regression for Diabetic Retinopathy Grading: A Quantified Assessment of Cross-Domain Generalization
Tuwaiq Academy（图瓦伊克学院）；Tuwaiq Research and Development Centre（图瓦伊克研究与发展中心）
Explainable fetal ultrasound quality assessment with progressive concept bottleneck models
Technical University of Denmark（丹麦技术大学）；Slagelse Hospital（斯莱格瑟医院）；CAMES；Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院丽格医院）；University of Copenhagen（哥本哈根大学）
