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arXiv 2023年

作者： Qiu, Jianing Li, Lin Sun, Jiankai Peng, Jiachuan Shi, Peilun Zhang, Ruiyang Dong, Yinzhao Lam, Kyle Lo, Frank P.-W. Xiao, Bo Yuan, Wu Wang, Ningli Xu, Dong Lo, Benny The Department of Computing Imperial College London United Kingdom The Department of Biomedical Engineering The Chinese University of Hong Kong Hong Kong Ltd. Hong Kong The Department of Informatics King’s College London United Kingdom The School of Engineering Stanford University United States The Department of Engineering Science University of Oxford United Kingdom The Faculty of Engineering The University of Hong Kong Hong Kong The Department of Surgery and Cancer Imperial College London United Kingdom The Hamlyn Centre for Robotic Surgery Imperial College London United Kingdom Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University China Beijing Ophthalmology & Visual Sciences Key Laboratory Beijing China The Department of Electrical Engineering and Computer Science United States The Christopher S. Bond Life Sciences Center University of Missouri United States The Facualty of Medicine Imperial College London United Kingdom

Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people’s imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our lives. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multi-modal data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents a comprehensive review of large AI models, from background to their applications. We identify seven key sectors in which large AI models are applicable and might have substantial influence, including 1) bioinformatics;2) medical diagnosis;3) medical imaging;4) medical © 2023, CC BY-NC-ND.

关键词： Medical imaging

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Codebook-Based Beam Tracking for Conformal Array-Enabled UAV MmWave Networks

arXiv

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arXiv 2020年

作者： Zhang, Jinglin Xu, Wenjun Gao, Hui Pan, Miao Han, Zhu Zhang, Ping Key Lab of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

Millimeter wave (mmWave) communications can potentially meet the high data-rate requirements of unmanned aerial vehicle (UAV) networks. However, as the prerequisite of mmWave communications, the narrow directional beam tracking is very challenging because of the three-dimensional (3D) mobility and attitude variation of UAVs. Aiming to address the beam tracking difficulties, we propose to integrate the conformal array (CA) with the surface of each UAV, which enables the full spatial coverage and the agile beam tracking in highly dynamic UAV mmWave networks. More specifically, the key contributions of our work are three-fold. 1) A new mmWave beam tracking framework is established for the CA-enabled UAV mmWave network. 2) A specialized hierarchical codebook is constructed to drive the directional radiating element (DRE)-covered cylindrical conformal array (CCA), which contains both the angular beam pattern and the subarray pattern to fully utilize the potential of the CA. 3) A codebook-based multiuser beam tracking scheme is proposed, where the Gaussian process machine learning enabled UAV position/attitude predication is developed to improve the beam tracking efficiency in conjunction with the tracking-error aware adaptive beamwidth control. Simulation results validate the effectiveness of the proposed codebook-based beam tracking scheme in the CA-enabled UAV mmWave network, and demonstrate the advantages of CA over the conventional planner array in terms of spectrum efficiency and outage probability in the highly dynamic scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Millimeter waves

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Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks

arXiv

引用

arXiv 2020年

作者： Kossen, Tabea Subramaniam, Pooja Madai, Vince I. Hennemuth, Anja Hildebrand, Kristian Hilbert, Adam Sobesky, Jan Livne, Michelle Galinovic, Ivana Khalil, Ahmed A. Fiebach, Jochen B. Frey, Dietmar CLAIM - Charité Lab for AI in Medicine Charité Universitätsmedizin Berlin Germany Department of Computer Engineering and Microelectronics Computer Vision & Remote Sensing Technical University Berlin Berlin Germany Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine Charité Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Bremen Germany Department VI Computer Science and Media Beuth University of Applied Sciences Berlin Germany Johanna-Etienne-Hospital Neuss Germany Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Mind Brain Body Institute Berlin School of Mind and Brain Humboldt University Berlin Berlin Germany Berlin Institute of Health Berlin Germany

Anonymization and data sharing are crucial for privacy protection and acquisition of large datasets for robust medical image analysis. This represents a major challenge, especially for brain imaging research. Here, the unique structure of brain images allows for potential re-identification and thus requires anonymization beyond conventional methods. Generative adversarial networks (GANs) have the potential to provide anonymous images while maintaining their predictive properties. Analyzing brain vessel segmentation as a use case, we trained 3 GAN architectures on time-of-flight (TOF) magnetic resonance angiography (MRA) patches of patients with cerebrovascular disease for image-label pair generation: 1) Deep convolutional GAN, 2) Wasserstein-GAN with gradient penalty (WGAN-GP) and 3) WGAN-GP with spectral normalization (WGAN-GP-SN). First, the synthesized image-labels from each GAN architecture were used to train a U-net for vessel segmentation. The U-nets were then tested on real patient data. In total, 66 patients were used for this analysis. In a second step, we simulated the application of our synthetic patches in a transfer learning approach using a second, independent dataset. Here, for an increasing number of up to 15 patients we evaluated vessel segmentation model performance on real data with and without pre-training on generated patches. Finally, performance for all models was assessed by the Dice Similarity Coefficient (DSC) and the 95th percentile of the Hausdorff Distance (95HD). Comparing the 3 GAN architectures, the U-net model trained on synthetic data generated by the WGAN-GP-SN showed the highest performance to predict brain vessels (DSC/95HD 0.82/28.97) benchmarked by the U-net trained on real data (0.89/26.61). The transfer learning approach showed superior performance for the same GAN architecture compared to no pre-training, especially for one labeled patient only (DSC/95HD 0.91/25.68 compared to DSC/95HD 0.85/27.36). In a brain imaging segment

关键词： Image segmentation

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Probabilistic load-margin assessment using vine copula and gaussian process emulation

arXiv

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arXiv 2020年

作者： Xu, Yijun Karra, Kiran Mili, Lamine Korkali, Mert Chen, Xiao Hu, Zhixiong Bradley Department of Electrical and Computer Engineering Virginia Tech Northern Virginia Center Falls ChurchVA22043 United States Applied Physics Lab Johns Hopkins University BaltimoreMD21218 United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Statistics University of California-Santa Cruz Santa CruzCA95064 United States

The increasing penetration of renewable energy along with the variations of the loads bring large uncertainties in the power system states that are threatening the security of power system planning and operation. Facing these challenges, this paper proposes a cost-effective, nonparametric method to quantify the impact of uncertain power injections on the load margins. First, we propose to generate system uncertain inputs via a novel vine copula due to its capability in simulating complex multivariate highly dependent model inputs. Furthermore, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. The simulations conducted on the IEEE 57-bus system, to which correlated renewable generation are attached, reveal the excellent performance of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

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CG-SENSE revisited: Results from the first ISMRM reproducibility challenge

arXiv

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arXiv 2020年

作者： Maier, Oliver Baete, Steven H. Fyrdahl, Alexander Hammernik, Kerstin Harrevelt, Seb Kasper, Lars Karakuzu, Agah Loecher, Michael Patzig, Franz Tian, Ye Wang, Ke Gallichan, Daniel Uecker, Martin Knoll, Florian Institute of Medical Engineering Graz University of Technology Graz Austria Center for Biomedical Imaging New York University School of Medicine New YorkNY United States Department of Clinical Physiology Karolinska University Hospital Karolinska Institutet Stockholm Sweden Department of Computing Imperial College London London United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands Institute for Biomedical Engineering ETH Zurich University of Zurich Zurich Switzerland Translational Neuromodeling Unit Institute for Biomedical Engineering University of Zurich ETH Zurich Zurich Switzerland NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montréal Montréal Canada Department of Radiology Stanford University StanfordCA United States Department of Radiology and Imaging Sciences University of Utah Salt Lake CityUT United States Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los AngelesCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Cardiff University Brain Research Imaging Centre Cardiff United Kingdom Institute for Diagnostic and Interventional Radiology University Medical Center Göttingen Göttingen Germany Germany University of Göttingen Germany University of Göttingen Germany

Purpose The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. Methods The task of the challenge was to reconstruct radially acquired multi-coil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Results visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. Discussion and Conclusion While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, e.g., density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient meta-data accompanying open data sets. Defining reproducibility quantitatively turned out to be non-trivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Deep Learning for Brain Age Estimation: A Systematic Review

arXiv

引用

arXiv 2022年

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Ser, Javier Del Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Assam Silchar788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Forensic Analysis of Synthetically Generated Western Blot Images

arXiv

引用

arXiv 2021年

作者： Mandelli, Sara Cozzolino, Davide Cannas, Edoardo Daniele Cardenuto, João Phillipe Moreira, Daniel Bestagini, Paolo Scheirer, Walter Rocha, Anderson Verdoliva, Luisa Tubaro, Stefano Delp, Edward J. The Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan20133 Italy The Dipartimento di Ingegneria Elettrica e Tecnologie Dell'Informazione University Federico Ii of Naples Naples80125 Italy The Dipartimento di Ingegneria Industriale University Federico Ii of Naples Naples80125 Italy The Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States The Artificial Intelligence Lab RECOD.ai Institute of Computing University of Campinas Campinas13083-970 Brazil The School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

The widespread diffusion of synthetically generated content is a serious threat that needs urgent countermeasures. As a matter of fact, the generation of synthetic content is not restricted to multimedia data like videos, photographs or audio sequences, but covers a significantly vast area that can include biological images as well, such as western blot and microscopic images. In this paper, we focus on the detection of synthetically generated western blot images. These images are largely explored in the biomedical literature and it has been already shown they can be easily counterfeited with few hopes to spot manipulations by visual inspection or by using standard forensics detectors. To overcome the absence of publicly available data for this task, we create a new dataset comprising more than 14K original western blot images and 24K synthetic western blot images, generated using four different state-of-the-art generation methods. We investigate different strategies to detect synthetic western blots, exploring binary classification methods as well as one-class detectors. In both scenarios, we never exploit synthetic western blot images at training stage. The achieved results show that synthetically generated western blot images can be spot with good accuracy, even though the exploited detectors are not optimized over synthetic versions of these scientific images. We also test the robustness of the developed detectors against post-processing operations commonly performed on scientific images, showing that we can be robust to JPEG compression and that some generative models are easily recognizable, despite the application of editing might alter the artifacts they leave. Copyright © 2021, The Authors. All rights reserved.

关键词： Image compression

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Fault-Tolerant Quaternion Neural Network Fast Terminal Sliding Mode Control for Guidance and Navigation of UAV’s under Actuator Faults

Fault-Tolerant Quaternion Neural Network Fast Terminal Slidi...

引用

International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Fernando E. Serrano Ahmad Taher Azar Saim Ahmed Nashwa Ahmad Kamal Ibraheem Kasim Ibraheem Instituto de Investigación en Energía Universidad Nacional Autónoma de Honduras Tegucigalpa Honduras Instituto de Robóticae Informática Industrial IRI-CSIC Universidad Politécnica de Cataluña Barcelona España College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Engineering Cairo University Giza Egypt Department of Electrical Engineering College of Engineering University of Baghdad Baghdad Iraq Department of Electronics and Communication Engineering Uruk University Baghdad Iraq

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

This research presents a terminal sliding mode controller and quaternion neural network observer for unmanned aerial vehicle (UAV) guidance and navigation under actuator fault. Initially, to determine which of a quadrotor’s four actuators is failing, a quaternion neural network observer is created. The quadrotor dynamic model includes the DC motor dynamics, which is taken into account when designing the fault-tolerant observer. The quaternion neural network presents four failure modes pertaining to an actuator state in which instability and divergence from the healthy state occur when one of the actuators fails. Next, a fault-tolerant terminal sliding mode controller is engineered to deliver the necessary controller action when all actuators are functioning normally, or if one or more quadrotor UAV actuators malfunction. The rapid terminal sliding mode controller is built in order to acquire the diverse control laws when one or many actuators fail or when the system is in healthy state. The suitable Lyapunov functional at various time instants is taken into account throughout the construction of this fault-tolerant controller to restore optimal performance. In addition to the theoretical outcomes of this investigation, a simulation is run to demonstrate the effectiveness of the observer/controller approach and to contrast it with other sliding mode and neural network observer/controller combinations. The research study’s conclusions and discussions are given at the end.

关键词： Fault diagnosis Actuators Fault tolerance Quaternions Fault tolerant systems Neural networks Observers

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EW-PESQ: A quality assessment method for speech signals sampled at 48 kHz

引用

AES: Journal of the Audio engineering Society 2010年第4期58卷 251-268页

作者： Bispo, Bruno C. Esquef, Paulo A.A. Biscainho, Luiz W.P. De Lima, Amaro A. Freeland, Fabio P. De Jesus, Rafael A. Said, Amir Lee, Bowon Schafer, Ronald W. Kalker, Ton Department of Electrical and Computer Engineering University of Porto Porto Portugal Coordination of Systems and Control National Laboratory for Scientific Computing LNCC/MCT Petrópolis RJ Brazil LPS-PEE/COPPE Federal University of Rio de Janeiro Rio de Janeiro RJ Brazil Nova Iguaçu RJ Brazil Rio de Janeiro RJ Brazil PETROBRAS/E and P Rio de Janeiro RJ Brazil Multimedia Communications and Networking Lab Hewlett-Packard Laboratories Palo Alto CA United States

Extended models of the W-PESQ method are presented which are capable of assessing the subjective quality of noisy speech signals sampled at 48 kHz. Two methods are proposed: a version backward compatible with the W-PESQ standard, called EW-PESQ(E), and another, called EW-PESQ(R), in which the original psychoacoustic models are entirely replaced with alternative formulations available in the literature. Performance figures of both the EW-PESQ(E) and the EW-PESQ(R) in predicting the subjective quality of extrawide-band speech (sampled at 48 kHz) corrupted with broad-band and environmental noise are reported. In both cases the results obtained reveal a strong correlation (approximately 97%) with mean opinion scores.

关键词： Speech communication

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GAMMA Challenge: Glaucoma grAding from Multi-Modality imAges

arXiv

引用

arXiv 2022年

作者： Wu, Junde Fang, Huihui Li, Fei Fu, Huazhu Lin, Fengbin Li, Jiongcheng Huang, Yue Yu, Qinji Song, Sifan Xu, Xinxing Xu, Yanyu Wang, Wensai Wang, Lingxiao Lu, Shuai Li, Huiqi Huang, Shihua Lu, Zhichao Ou, Chubin Wei, Xifei Liu, Bingyuan Kobbi, Riadh Tang, Xiaoying Lin, Li Zhou, Qiang Hu, Qiang Bogunović, Hrvoje Orlando, José Ignacio Zhang, Xiulan Xu, Yanwu State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China Singapore School of Informatics Xiamen University Xiamen China Shanghai Jiao Tong University Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Institute of High Performance Computing A*STAR Singapore Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin China School of Medical Technology Beijing Institute of Technology Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Weizhi Medical Technology Company Suzhou China École de Technologie Supérieure Montreal Montreal Canada DIAGNOS Inc. Quebec Canada Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Technology Co. Ltd. Shanghai China School of Information and Electronics Beijing Institute of Technology Beijing China Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology Medical University of Vienna Austria Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfill the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyze their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniqu

关键词： Grading

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