The screen content images (SCIs) usually comprise various content types with sharp edges, in which the artifacts or distortions can be well sensed by the vanilla structure similarity measurement in a full reference manner. Nonetheless, almost all of the current SOTA structure similarity metrics are "locally" formulated in a single-level manner, while the true human visual system (HVS) follows the multi-level manner, and such mismatch could eventually prevent these metrics from achieving trustworthy quality assessment. To ameliorate, this paper advocates a novel solution to measure structure similarity "globally" from the perspective of sparse representation. To perform multi-level quality assessment in accordance with the real HVS, the above-mentioned global metric will be integrated with the conventional local ones by resorting to the newly devised selective deep fusion network. To validate its efficacy and effectiveness, we have compared our method with 12 SOTA methods over two widely-used large-scale public SCI datasets, and the quantitative results indicate that our method yields significantly higher consistency with subjective quality score than the currently leading works. Both the source code and data are also publicly available to gain widespread acceptance and facilitate new advancement and its validation.
Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment
South China Normal University（华南师范大学）；City University of Hong Kong（香港城市大学）；City University of Hong Kong, Shenzhen Research Institute（香港城市大学深圳研究院）；Lingnan University（岭南大学）
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）
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）
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）
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）
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）
CAS-IQA: Teaching Vision-Language Models for Synthetic Angiography Quality Assessment
State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology Beijing（北京科技大学）；Hunan University of Science and Technology（湖南科技大学）；Shanxi University of Finance and Economics（山西财经大学）；Beijing Friendship Hospital, Capital Medical University（首都医科大学北京友谊医院）
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）
RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement
Monash University Malaysia（莫纳什大学马来西亚分校）；The University of Oklahoma（俄克拉荷马大学）；Karolinska Institute（卡罗林斯卡学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；University of Malaya（马来亚大学）；The University of Melbourne（墨尔本大学）
A Scale-adaptive Vision Model Links C. elegans Neuronal Morphology to Behavior for Neurotoxicity Assessment
Zhejiang University School of Medicine（浙江大学医学院）；Second Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第二医院）；Zhejiang University（浙江大学）；Guizhou University（贵州大学）；Fourth Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第四医院）；Liangzhu Laboratory（良渚实验室）
