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Cosmological constraints from the tomography of DES-Y3 galaxies with CMB lensing from ACT DR4

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arXiv 2023年

作者： Marques, G.A. Madhavacheril, M.S. Darwish, O. Shaikh, S. Aguena, M. Alves, O. Avila, S. Bacon, D. Baxter, E.J. Bechtol, K. Becker, M.R. Bertin, E. Blazek, J. Bond, J. Richard Brooks, D. Cai, H. Calabrese, E. Rosell, A. Carnero Kind, M. Carrasco Carretero, J. Cawthon, R. Crocce, M. da Costa, L.N. Pereira, M.E.S. De Vicente, J. Desai, S. Diehl, H.T. Doel, P. Doux, C. Drlica-Wagner, A. Dunkley, J. Elvin-Poole, J. Everett, S. Ferraro, Simone Ferrero, I. Flaugher, B. Fosalba, P. García-Bellido, J. Gatti, M. Giannini, G. Gluscevic, V. Gruen, D. Gruendl, R.A. Gutierrez, G. Harrison, I. Hill, J. Colin Hinton, S.R. Hollowood, D.L. Honscheid, K. Huterer, D. Jeffrey, N. Kim, J. Kuehn, K. Lahav, O. Lemos, P. Lima, M. Huffenberger, K.M. MacCrann, N. Marshall, J.L. Mena-Fernández, J. Miquel, R. Mohr, J.J. Moodley, K. Muir, J. Naess, S. Nati, F. Page, L.A. Palmese, A. Malagón, A.A. Plazas Porredon, A. Prat, J. Qu, F.J. Raveri, M. Ross, A.J. Rykoff, E.S. Farren, G.S. Samuroff, S. Sanchez, E. Schubnell, M. Sehgal, N. Sevilla-Noarbe, I. Sheldon, E. Sherwin, B.D. Sifón, C. Smith, M. Spergel, D.N. Staggs, S.T. Suchyta, E. Tarle, G. To, C. Van Engelen, A. Weaverdyck, N. Weller, J. Wenzl, L. Wiseman, P. Wollack, E.J. Yanny, B. Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Department of Physics Florida State University TallahasseeFL32306 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Centre for the Universe Perimeter Institute WaterlooONN2L 2Y5 Canada Université de Genève Département de Physique Théorique Centre for Astroparticle Physics 24 quai Ernest-Ansermet Genève 4CH-1211 Switzerland School of Earth and Space Exploration Arizona State University TempeAZ85287 United States Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Department of Physics University of Michigan Ann ArborMI48109 United States The Barcelona Institute of Science and Technology Campus UAB Barcelona Bellaterra08193 Spain Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom Institute for Astronomy University of Hawai’i 2680 Woodlawn Drive HonoluluHI96822 United States Physics Department 2320 Chamberlin Hall University of Wisconsin-Madison 1150 University Avenue MadisonWI53706-1390 United States Argonne National Laboratory 9700 South Cass Avenue LemontIL60439 United States CNRS UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France CITA University of Toronto TorontoONM5S 3H8 Canada Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Department of Physics and Astronomy University of Pittsburgh PittsburghPA15260 United States School of Physics and Astronomy Cardiff University The Parade CardiffCF24 3AA United Kingdom Instituto de Astrofisica de Canarias Tenerife La LagunaE-38205 Spain Universidad de La Laguna

We present a measurement of the cross-correlation between the MAGLIM galaxies selected from the Dark Energy Survey (DES) first three years of observations (Y3) and cosmic microwave background (CMB) lensing from the Atacama Cosmology Telescope (ACT) data Release 4 (DR4), reconstructed over ∼ 436 deg2 of the sky. Our galaxy sample, which covers ∼ 4143 deg2, is divided into six redshift bins spanning the redshift range of 0.20 8 compared to results from measurements of CMB anisotropies by the Planck satellite, although at a mild level ( Copyright © 2023, The Authors. All rights reserved.

关键词： Galaxies

Smart Annotation Tool for Multi-sensor Gait-based Daily Activity data

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Smart Annotation Tool for Multi-sensor Gait-based Daily Acti...

IEEE International Conference on Pervasive Computing and Communications

作者： Christine F. Martindale Nils Roth Julius Hannink Sebastijan Sprager Bjoern M. Eskofier Machine Learning and Data Analytics Lab Computer Science Department Friedrich-Alexander University Erlangen-Nürnberg Germany Faculty of Computer and Information Science University of Ljubljana Slovenia

ISBN: (纸本)9781538632284

The monitoring of patients within a natural, home environment is important in order to close knowledge gaps in the treatment and care of neurodegenerative diseases, such as quantifying the daily fluctuation of Parkinson's patients' symptoms. The combination of machine learning algorithms and wearable sensors for gait analysis is becoming capable of achieving this. However, these algorithms require large, labelled, realistic datasets for training. Most systems used as a ground truth for labelling are restricted to the laboratory environment, as well as being large and expensive. We propose a study design for a realistic activity monitoring dataset, collected with inertial measurement units, pressure insoles and cameras. It is not restricted by a fixed location or capture volume and still enables the labelling of gait phases or, where non-gait movement such as jumping occur: on-the-ground, off-the-ground phases. Additionally, this paper proposes a smart annotation tool which reduces annotation cost by more than 80%. This smart annotation is based on edge detection within the pressure sensor signal. The tool also enables annotators to perform assisted correction of these labels in a post-processing step. This system enables the collection and labelling of large, fairly realistic datasets where 93% of the automatically generated labels are correct and only an additional 10% need to be inserted manually. Our tool and protocol, as a whole, will be useful for efficiently collecting the large datasets needed for training and validation of algorithms capable of cyclic human motion analysis in natural environments.

关键词： Image edge detection Tools Cameras Labeling Pressure sensors Intelligent sensors Edge detection Pressure Sensor Device Component Cameras intelligent sensors ANNOTATIONS laboratory environment dataset Wearable sensors Measurement units Research Design INSOLE Packaging

Spatial 3D Matern priors for fast Whole-Brain fMRI analysis

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arXiv 2019年

作者： Siden, Per Lindgren, Finn Bolinz, David Eklund, Anders Villani, Mattias Division of Statistics and Machine Learning Dept. of Computer and Information Science Linkoping University LinkopingSE-58183 School of Mathematics University of Edinburgh Peter Guthrie Tait Road EdinburghEH93FD United Kingdom Division of Applied Mathematics and Statistics Dept. of Mathematical Sciences Chalmers and University of Gothenburg GoteborgSE-41296 Division of Medical Informatics Dept. of Biomedical Engineering Center for Medical Image Science and Visualization Linkoping University LinkopingSE-58183 Department of Statistics Stockholm University StockholmSE-10691 Sweden

Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors have been shown to produce state-of-the-art activity maps without pre-smoothing the data. The proposed inference algorithms are computationally demanding however, and the proposed spatial priors have several less appealing properties, such as being improper and having infinite spatial range. Our paper proposes a statistical inference framework for functional magnetic resonance imaging (fMRI) analysis based on the class of Matern covariance functions. The framework uses the Gaussian Markov random field (GMRF) representation of Matern fields via the stochastic partial difierential equation (SPDE) approach of Lindgren et al. (2011). This allows for more exible and interpretable spatial priors, while maintaining the sparsity required for fast inference in the high-dimensional whole-brain setting. We develop an accelerated stochastic gradient descent (SGD) optimization algorithm for empirical Bayes (EB) inference of the spatial hyperparameters. Conditional on the inferred hyperparameters, we make a fully Bayesian treatment of the main parameters of interest, that is, the brain activity coeficients. We apply the Matern prior to both experimental and simulated task-fMRI data and clearly demonstrate that this is a more reasonable choice than the previously used priors, by using prior simulation, cross validation and visual inspection of the resulting activation maps. Additionally, to illustrate the potential of the SPDE formulation, we derive an anisotropic version of the Matern 3D prior. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

The importance of transparency and reproducibility in artificial intelligence research

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arXiv 2020年

作者： Haibe-Kains, Benjamin Adam, George Alexandru Hosny, Ahmed Khodakarami, Farnoosh Waldron, Levi Wang, Bo McIntosh, Chris Kundaje, Anshul Greene, Casey S. Hoffman, Michael M. Leek, Jeffrey T. Huber, Wolfgang Brazma, Alvis Pineau, Joelle Tibshirani, Robert Hastie, Trevor Ioannidis, John P.A. Quackenbush, John Aerts, Hugo J.W.L. Princess Margaret Cancer Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Ontario Institute for Cancer Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Radiation Oncology and Radiology Dana-Farber Cancer Institute Brigham and Women's Department of Epidemiology and Biostatistics Institute for Implementation Science in Population Health CUNY Graduate School of Public Health and Health Policy New YorkNY United States Peter Munk Cardiac Centre University Health Network TorontoON Canada Hospital Harvard Medical School BostonMA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Dept. of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Childhood Cancer Data Lab Alex's Lemonade Stand Foundation PhiladelphiaPA United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute EMBL-EBI Hinxton United Kingdom McGill University MontrealQC Canada Montreal Institute for Learning Algorithms QC Canada StanfordCA United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Departments of Medicine of Health Research and Policy and of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Statistics Stanford University School of Humanities and Sciences StanfordCA United States Department of Biostatistics Harvard T.H Chan School of Public Health BostonMA Un

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field. Copyright © 2020, The Authors. All rights reserved.

关键词： Artificial intelligence

Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research

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Digital Biomarkers 2019年第3期3卷 116-132页

作者： Badawy, Reham Hameed, Farhan Bataille, Lauren Little, Max A. Claes, Kasper Saria, Suchi Cedarbaum, Jesse M. Stephenson, Diane Neville, Jon Maetzler, Walter Espay, Alberto J. Bloem, Bastiaan R. Simuni, Tanya Karlin, Daniel R. School of Computer Science University of Birmingham Birmingham United Kingdom Digital Medicine and Pfizer Innovation Research Lab Early Clinical Development Pfizer Inc. CambridgeMA United States College of Computer and Information Science Northeastern University BostonMA United States Analytics Informatics and Business Intelligence Chief Digital Office Pfizer Inc. New YorkNY United States Michael J. Fox Foundation for Parkinson's Research New YorkNY United States Media Lab Massachusetts Institute of Technology CambridgeMA United States UCB Biopharma Brussels Belgium Machine Learning and Healthcare Laboratory Departments of Computer Science Statistics and Health Policy Malone Center for Engineering in Healthcare Armstrong Institute for Patient Safety and Quality Johns Hopkins University BaltimoreMD United States Biogen CambridgeMA United States Critical Path Institute TucsonAZ United States Clinical Data Interchange Standards Consortium AustinTX United States Department of Neurology Christian Albrecht University Kiel Germany James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders University of Cincinnati CincinnatiOH United States Department of Neurology Donders Institute for Brain Cognition and Behavior Radboud University Medical Center Nijmegen Netherlands Department of Neurology Gardner Center for Parkinson's Disease and Movement Disorders UC Gardner Neuroscience Institute University of Cincinnati CincinnatiOH United States Tufts University School of Medicine BostonMA United States HealthMode New YorkNY United States School of Engineering and Applied Science Aston University BirminghamB47ET United Kingdom

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape. © 2019 The Author(s

关键词： Metadata

Unintended consequences: Factors influencing oil palm plantation expansion

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IOP Conference Series: Earth and Environmental science 2020年第1期538卷

作者： S Wongsai J Keson N Wongsai Thammasat University Research Unit in Data Learning Division of Mathematics and Statistics Faculty of Science and Technology Thammasat University Pathumthani 12120 Thailand Faculty of Technology and Environment Prince of Songkla University Phuket Campus Phuket 83120 Thailand Department of Mathematics and Computer Science Faculty of Science and Technology Prince of Songkla University Pattani Campus Pattani 94000 Thailand

We investigated the landscape variables affecting the current dramatic expansion of oil palm plantations in Lam Thap district Krabi Province Thailand. THEOS satellite data was used to map land use classifications using the support vector machine method. Seven land use classes were consolidated into oil palm plantations and other rural uses. A logistic regression model was then applied to search for the relationship between two land use classes and the landscape variables of slope (three classes) and soil drainage (four classes). Overall, slope and drainage were statistically significant in explaining oil palm plantation expansion. Flat areas with poor drainage were the strongest factors. This contradicts the known optimal agronomic requirements for oil palms of well drainage. We suggest that what is observed is an unintended consequence of government oil palm support policies.

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On ADMM in deep learning: Convergence and saturation-avoidance

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arXiv 2019年

作者： Zeng, Jinshan Lin, Shao-Bo Yao, Yuan Zhou, Ding-Xuan School of Computer and Information Engineering Jiangxi Normal University Nanchang China Liu Bie Ju Centre for Mathematical Sciences City University of Hong Kong Hong Kong Hong Kong Department of Mathematics Hong Kong University of Science and Technology Hong Kong Hong Kong Center of Intelligent Decision-Making and Machine Learning School of Management Xi’an Jiaotong University Xi’an China School of Data Science Department of Mathematics City University of Hong Kong Hong Kong Hong Kong

In this paper, we develop an alternating direction method of multipliers (ADMM) for deep neural networks training with sigmoid-type activation functions (called sigmoid-ADMM pair), mainly motivated by the gradient-free nature of ADMM in avoiding the saturation of sigmoid-type activations and the advantages of deep neural networks with sigmoid-type activations (called deep sigmoid nets) over their rectified linear unit (ReLU) counterparts (called deep ReLU nets) in terms of approximation. In particular, we prove that the approximation capability of deep sigmoid nets is not worse than that of deep ReLU nets by showing that ReLU activation function can be well approximated by deep sigmoid nets with two hidden layers and finitely many free parameters but not vice-verse. We also establish the global convergence of the proposed ADMM for the nonlinearly constrained formulation of the deep sigmoid nets training from arbitrary initial points to a Karush-Kuhn-Tucker (KKT) point at a rate of order O(1/k). Besides sigmoid activation, such a convergence theorem holds for a general class of smooth activations. Compared with the widely used stochastic gradient descent (SGD) algorithm for the deep ReLU nets training (called ReLU-SGD pair), the proposed sigmoid-ADMM pair is practically stable with respect to the algorithmic hyperparameters including the learning rate, initial schemes and the pro-processing of the input data. Moreover, we find that to approximate and learn simple but important functions the proposed sigmoid-ADMM pair numerically outperforms the ReLU-SGD pair. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical activation

Generating diffusion MRI scalar maps from T1 weighted images using generative adversarial networks

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arXiv 2018年

作者： Gu, Xuan Knutsson, Hans Nilsson, Markus Eklund, Anders Division of Medical Informatics Department of Biomedical Engineering Division of Statistics and Machine learning Department of Computer and Information Science Linköping University Linköping Sweden Department of Clinical Sciences Radiology Lund University Lund Sweden

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Biomedical image analysis competitions: The state of current participation practice

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking