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Technology, Engineering, and Computing Applications (ICTECA), International Conference on

作者： Gamal Kusuma Zamahsari Sahrul Romadhon Mufidah Nur Amalia Lailatul Rifah Arti Prihatini Ardi Wina Saputra Digital Language Learning Center Communication Science Department Faculty of Humanities Bina Nusantara University Jakarta Indonesia Indonesian Language Education Study Program Institute of Islamic Religion Madura Pamekasan Indonesia Digital Language Learning Center Computer Science Department Faculty of Humanities Bina Nusantara University Jakarta Indonesia Digital Language Learning Center Visual Communication Design Faculty of Humanities Bina Nusantara University Jakarta Indonesia Indonesian Language Education Faculty of Teacher Training and Education Unversity of Muhammadiyah Malang Malang Indonesia Indonesian Language Education Study Program Widya Mandala Catholic University Surabaya Indonesia

Recent advancements in e-learning have highlighted the significance of incorporating gamification into language acquisition processes. It is essential to examine the application of learning game features within classroom settings. The foremost goal of this systematic review of literature is to ascertain and thoroughly delineate the elements of gamification considered suitable for incorporation into environments for language learning. This investigation focuses on two main objectives: firstly, to examine the influence of diverse elements of gamification on the enhancement of language abilities, and secondly, to comprehend how these elements shape the educational approaches and experiences of students in a gamified learning environment. The review methodology employed in this study is a systematic literature review of the current literature to comprehensively analyze and map out the effective utilization of gamification in the context of language teaching. The results of our study demonstrate that the deliberate integration of game features can have a substantial impact on student motivation and engagement. Nevertheless, the efficacy of these components is contingent upon the individual attributes of students and the educational goals in question. The paper finishes by providing advice for customizing gamification tactics to accommodate the varying learner profiles in language teaching. Additionally, it suggests potential areas of future research that can contribute to the ongoing development and enhancement of this pedagogical approach.

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Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

arXiv

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arXiv 2024年

作者： Li, Haopeng Deng, Andong Liu, Jun Rahmani, Hossein Guo, Yulan Schiele, Bernt Bennamoun, Mohammed Ke, Qiuhong School of Computing and Information Systems University of Melbourne Australia Center for Research in Computer Vision University of Central Florida United States Pillar Singapore University of Technology and Design Singapore School of Computing and Communications Lancaster University United Kingdom School of Electronics and Communication Engineering Sun Yat-sen University China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Germany School of Physics Maths and Computing University of Western Australia Australia Department of Data Science & AI Monash University Australia

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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Physics-informed Bayesian optimization of variational quantum circuits 23

Physics-informed Bayesian optimization of variational quantu...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Kim A. Nicoli Christopher J. Anders Lena Funcke Tobias Hartung Karl Jansen Stefan Kühn Klaus-Robert Müller Paolo Stornati Pan Kessel Shinichi Nakajima Transdisciplinary Research Area (TRA) Matter University of Bonn Germany and Helmholtz Institute for Radiation and Nuclear Physics (HISKP) and Berlin Institute for the Foundations of Learning and Data (BIFOLD) Berlin Institute for the Foundations of Learning and Data (BIFOLD) and Technische Universität Berlin Germany Transdisciplinary Research Area (TRA) Matter University of Bonn Germany and Helmholtz Institute for Radiation and Nuclear Physics (HISKP) Northeastern University London UK and Khoury College of Computer Sciences Northeastern University CQTA Deutsches Elektronen-Synchrotron (DESY) Zeuthen Germany Berlin Institute for the Foundations of Learning and Data (BIFOLD) and Technische Universität Berlin Germany and Department of Artificial Intelligence Korea University Korea and Max Planck Institut für Informatik Saarbrücken Germany Institute of Photonic Sciences The Barcelona Institute of Science and Technology (ICFO) Prescient Design gRED Roche Switzerland Berlin Institute for the Foundations of Learning and Data (BIFOLD) and Technische Universität Berlin Germany and RIKEN Center for AIP Japan

In this paper, we propose a novel and powerful method to harness Bayesian optimization for Variational Quantum Eigensolvers (VQEs)—a hybrid quantum-classical protocol used to approximate the ground state of a quantum Hamiltonian. Specifically, we derive a VQE-kernel which incorporates important prior information about quantum circuits: the kernel feature map of the VQE-kernel exactly matches the known functional form of the VQE's objective function and thereby significantly reduces the posterior uncertainty. Moreover, we propose a novel acquisition function for Bayesian optimization called Expected Maximum Improvement over Confident Regions (EMICoRe) which can actively exploit the inductive bias of the VQE-kernel by treating regions with low predictive uncertainty as indirectly "observed". As a result, observations at as few as three points in the search domain are sufficient to determine the complete objective function along an entire one-dimensional subspace of the optimization landscape. Our numerical experiments demonstrate that our approach improves over state-of-the-art baselines.

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Improving Cardiovascular Disease Prediction With Machine learning Using Mental Health Data: A Prospective UK Biobank Study

JACC: Advances

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JACC: Advances 2024年第9期3卷 101180-101180页

作者： Dorraki, Mohsen Liao, Zhibin Abbott, Derek Psaltis, Peter J. Baker, Emma Bidargaddi, Niranjan Wardill, Hannah R. van den Hengel, Anton Narula, Jagat Verjans, Johan W. School of Computer and Mathematical Sciences The University of Adelaide Adelaide Australia Australian Institute for Machine Learning (AIML) Adelaide Australia Lifelong Health Theme South Australian Health and Medical Research Institute (SAHMRI) Adelaide Australia School of Electrical & Electronic Engineering University of Adelaide Adelaide Australia Faculty of Health and Medical Sciences The University of Adelaide Adelaide Australia Department of Cardiology Central Adelaide Local Health Network Adelaide Australia College of Medicine and Public Health Flinders University Adelaide Australia Precision Cancer Medicine Theme South Australian Health and Medical Research Institute (SAHMRI) Adelaide Australia School of Biomedicine Faculty of Health and Medical Sciences University of Adelaide Adelaide Australia University of Texas Health Science Center Houston United States

Background: Robust and accurate prediction of cardiovascular disease (CVD) risk facilitates early intervention to benefit patients. The intricate relationship between mental health disorders and CVD is widely recognized. However, existing models often overlook psychological factors, relying on a limited set of clinical and lifestyle parameters, or being developed on restricted population subsets. Objectives: This study aims to assess the impact of integrating psychological data into a novel machine learning (ML) approach on enhancing CVD prediction performance. Methods: Using a comprehensive UK Biobank data set (n = 375,145), the correlation between CVD and traditional and psychological risk factors was examined. CVD included hypertensive disease, ischemic heart disease, heart failure, and arrhythmias. An ensemble ML model containing 5 constituent algorithms (decision tree, random forest, XGBoost, support vector machine, and deep neural network) was tested for its ability to predict CVD based on 2 training data sets: using traditional CVD risk factors alone, or using a combination of traditional and psychological risk factors. Results: A total of 375,145 subjects with normal health status and with CVD were included. The ensemble ML model could predict CVD with 71.31% accuracy using traditional CVD risk factors alone. However, by adding psychological factors to the training data, accuracy increased to 85.13%. The accuracy and robustness of the ensemble ML model outperformed all 5 constituent learning algorithms. Conclusions: Incorporating mental health assessment data within an ensemble ML model results in a significantly improved, highly accurate, CVD prediction model, outperforming traditional risk factor prediction alone. © 2024 The Authors

关键词： artificial intelligence cardiovascular disease cardiovascular prediction machine learning mental health

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Methodology for creation of labeled image datasets of entrained air voids and aggregates in concrete surfaces using confocal laser scanning microscopy

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Advanced Engineering Informatics 2025年 67卷

作者： Viktor Kostic Qadeer Khan Daniel Cremers Jithender Timothy Thomas Kränkel Christoph Gehlen TUM School of Engineering and Design Department of Materials Engineering Chair of Materials Science and Testing Centre for Building Materials (cbm) Technical University of Munich Munich 81245 BY Germany TUM School of Computation Information and Technology Computer Vision Group Technical University of Munich Munich 81245 BY Germany Munich Center for Machine Learning Munich 81245 BY Germany

Several strategies using neural network algorithms have been established to automate the process of evaluating parameters regarding the freeze–thaw resistance of hardened concrete from image data. However, the performance of such models is dependent on the quality of the datasets used for training. Hand-labeling concrete image data is challenging, where especially small micro air voids (<300 μ m) can be overlooked. This potentially leads to algorithms that are unable to accurately detect the micro air void content, despite being required by current standards (e.g. DIN EN 480-11). To account for this, a method was developed using confocal laser scanning microscopy and the “Segment Anything” model. Acquiring surface height-data through confocal laser scanning microscopy expendites the labeling process and creates more reliable training data due to the detectability of air voids through height differences. Furthermore, by combining the height-data with RGB-images it was possible to accurately distinguish between air voids, aggregates and cement paste with high repeatability between repeated labeling iterations. Keeping high magnification levels (120-480x) enabled the detection of air voids down to 5 μ m. The framework and dataset presented in this study are fundamental for further development of new neural network based algorithms, specialized for the characterization and evaluation of the freeze–thaw resistance of concrete.

关键词： Concrete Freeze–thaw resistance Air voids Labeling Confocal laser scanning microscopy Segment anything model

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Community Action on FAIR Data will Fuel a Revolution in Materials Research

arXiv

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arXiv 2022年

作者： Catherine Brinson, L. Bartolo, Laura M. Blaiszik, Ben Elbert, David Foster, Ian Strachan, Alejandro Voorhees, Peter W. Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Data and Databases Center for Hierarchical Materials Design Northwestern University EvanstonIL60201 United States Globus University of Chicago ChicagoIL60637 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Materials Data Facility Hopkins Extreme Materials Institute Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science University of Chicago United States Materials Engineering Purdue University West LafayetteIN47907 United States nanoHUB Purdue University West LafayetteIN47907 United States Materials Science and Engineering Northwestern University EvanstonIL60201 United States Center for Hierarchical Materials Design Northwestern University EvanstonIL60201 United States Northwestern Argonne Institute for Science and Engineering Northwestern University EvanstonIL60201 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60201 United States

Widely shared and accessible materials data are the key to a world in which accelerated materials development addresses society's greatest challenges. We present a roadmap for connected materials data to enable re...

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Eye tracking: empirical foundations for a minimal reporting guideline

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Behavior research methods 2023年第1期55卷 364-416页

作者： Kenneth Holmqvist Saga Lee Örbom Ignace T C Hooge Diederick C Niehorster Robert G Alexander Richard Andersson Jeroen S Benjamins Pieter Blignaut Anne-Marie Brouwer Lewis L Chuang Kirsten A Dalrymple Denis Drieghe Matt J Dunn Ulrich Ettinger Susann Fiedler Tom Foulsham Jos N van der Geest Dan Witzner Hansen Samuel B Hutton Enkelejda Kasneci Alan Kingstone Paul C Knox Ellen M Kok Helena Lee Joy Yeonjoo Lee Jukka M Leppänen Stephen Macknik Päivi Majaranta Susana Martinez-Conde Antje Nuthmann Marcus Nyström Jacob L Orquin Jorge Otero-Millan Soon Young Park Stanislav Popelka Frank Proudlock Frank Renkewitz Austin Roorda Michael Schulte-Mecklenbeck Bonita Sharif Frederick Shic Mark Shovman Mervyn G Thomas Ward Venrooij Raimondas Zemblys Roy S Hessels Department of Psychology Nicolaus Copernicus University Torun Poland. kenneth.holmqvist@ur.de. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. Experimental Psychology Helmholtz Institute Utrecht University Utrecht The Netherlands. Lund University Humanities Lab and Department of Psychology Lund University Lund Sweden. Department of Ophthalmology SUNY Downstate Health Sciences University Brooklyn NY USA. Tobii Pro AB Danderyd Sweden. Social Health and Organizational Psychology Utrecht University Utrecht The Netherlands. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. TNO Soesterberg The Netherlands. Department of Ergonomics Leibniz Institute for Working Environments and Human Factors Dortmund Germany. Institute of Informatics LMU Munich Munich Germany. Institute of Child Development University of Minnesota Minneapolis USA. School of Psychology University of Southampton Southampton UK. School of Optometry and Vision Sciences Cardiff University Cardiff UK. Department of Psychology University of Bonn Bonn Germany. Vienna University of Economics and Business Vienna Austria. Department of Psychology University of Essex Essex UK. Department of Neuroscience Erasmus MC Rotterdam The Netherlands. Machine Learning Group Department of Computer Science IT University of Copenhagen Copenhagen Denmark. SR Research Ltd Ottawa Canada. Human-Computer Interaction University of Tübingen Tübingen Germany. University of British Columbia Columbia Canada. Department of Eye and Vision Science Institute of Life Course and Medical Sciences University of Liverpool Liverpool UK. Department of Education and Pedagogy Division Education Faculty of Social and Behavioral Sciences Ut

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

关键词： Data quality Eye movements Eye tracking Replicability Reporting guidelines Reporting practices Reporting standards Reproducibility

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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AstroCLIP: A Cross-Modal Foundation Model for Galaxies

arXiv

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arXiv 2023年

作者： Parker, Liam Lanusse, Francois Golkar, Siavash Sarra, Leopoldo Cranmer, Miles Bietti, Alberto Eickenberg, Michael Krawezik, Geraud McCabe, Michael Ohana, Ruben Pettee, Mariel Blancard, Bruno Régaldo-Saint Tesileanu, Tiberiu Cho, Kyunghyun Ho, Shirley Morel, Rudy The Flatiron Institute 162 5th Ave New YorkNY10010 United States Department of Astronomy University of Cambridge Madingley Rd CambridgeCB3 0HA United Kingdom Department of Computer Science University of Colorado Boulder 430 UCB 1111 Engineering Dr BoulderCO80309 United States Lawrence Berkeley National Laboratory 1 Cyclotron Rd BerkeleyCA94720 United States Center for Data Science New York University 60 5th Ave New YorkNY10011 United States Prescient Design Genentech 149 5th Ave New YorkNY10010 United States CIFAR Learning in Machines and Brains Fellow TorontoONM5G 1M1 Canada Department of Astrophysics Princeton University 4 Ivy Lane Princeton NJ08544 United States

We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used - without any model fine-tuning - for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift estimation, (3) galaxy property estimation from both images and spectra, and (4) morphology classification. Our approach to implementing AstroCLIP consists of two parts. First, we embed galaxy images and spectra separately by pretraining separate transformer-based image and spectrum encoders in self-supervised settings. We then align the encoders using a contrastive loss. We apply our method to spectra from the Dark Energy Spectroscopic Instrument and images from its corresponding Legacy Imaging Survey. Overall, we find remarkable performance on all downstream tasks, even relative to supervised baselines. For example, for a task like photometric redshift prediction, we find similar performance to a specifically-trained ResNet18, and for additional tasks like physical property estimation (stellar mass, age, metallicity, and sSFR), we beat this supervised baseline by 19% in terms of R2. We also compare our results to a state-of-the-art self-supervised single-modal model for galaxy images, and find that our approach outperforms this benchmark by roughly a factor of two on photometric redshift estimation and physical property prediction in terms of R2, while remaining roughly in-line in terms of morphology classification. Ultimately, our approach represents the first cross-modal self-supervised model for galaxies, and the first self-supervised transformer-based architectures for galaxy images and spectra. Copyright © 2023, The Authors. All rights reserved.

关键词： Galaxies

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Why is the winner the best?

arXiv

引用

arXiv 2023年

作者： Eisenmann, M. Reinke, A. Weru, V. Tizabi, M.D. Isensee, F. Adler, T.J. Ali, S. Andrearczyk, V. Aubreville, M. Baid, U. Bakas, S. Balu, N. Bano, S. Bernal, J. Bodenstedt, S. Casella, A. Cheplygina, V. Daum, M. de Bruijne, M. Depeursinge, A. Dorent, R. Egger, J. Ellis, D.G. Engelhardt, S. Ganz, M. Ghatwary, N. Girard, G. Godau, P. Gupta, A. Hansen, L. Harada, K. Heinrich, M. Heller, N. Hering, A. Huaulmé, A. Jannin, P. Kavur, A.E. Kodym, O. Kozubek, M. Li, J. Li, H. Ma, J. Martín-Isla, C. Menze, B. Noble, A. Oreiller, V. Padoy, N. Pati, S. Payette, K. Rädsch, T. Rafael-Patiño, J. Bawa, V. Singh Speidel, S. Sudre, C.H. van Wijnen, K. Wagner, M. Wei, D. Yamlahi, A. Yap, M.H. Yuan, C. Zenk, M. Zia, A. Zimmerer, D. Aydogan, D. Bhattarai, B. Bloch, L. Brüngel, R. Cho, J. Choi, C. Dou, Q. Ezhov, I. Friedrich, C.M. Fuller, C. Gaire, R.R. Galdran, A. Faura, Á. García Grammatikopoulou, M. Hong, S. Jahanifar, M. Jang, I. Kadkhodamohammadi, A. Kang, I. Kofler, F. Kondo, S. Kuijf, H. Li, M. Luu, M. Martinčič, T. Morais, P. Naser, M.A. Oliveira, B. Owen, D. Pang, S. Park, J. Park, S. Plotka, S. Puybareau, E. Rajpoot, N. Ryu, K. Saeed, N. Shephard, A. Shi, P. Štepec, D. Subedi, R. Tochon, G. Torres, H.R. Urien, H. Vilaça, J.L. Wahid, K.A. Wang, H. Wang, J. Wang, L. Wang, X. Wiestler, B. Wodzinski, M. Xia, F. Xie, J. Xiong, Z. Yang, S. Yang, Y. Zhao, Z. Maier-Hein, K. Jäger, P.F. Kopp-Schneider, A. Maier-Hein, L. Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Germany Heidelberg Germany School of Computing Faculty of Engineering and Physical Sciences University of Leeds Leeds United Kingdom Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington SeattleWA United States Department of Computer Science University College London London United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Sierre Switzerland Harvard Medical School Brigham and Women’s Hospital BostonMA United States School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Essen Germany University of Nebraska Medical Center OmahaNE United States Department of Internal Medicine III Heidelberg University Hospital Heidelberg Germany Neurobiology Research Unit Copenhagen University Hospital Rigshospitalet C

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. © 2023, CC BY.

关键词： Benchmarking

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