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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

引用

arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Massive stars exploding in a He-rich circumstellar medium − X. Flash spectral features in the Type Ibn SN 2019cj and observations of SN 2018jmt

arXiv

引用

arXiv 2024年

作者： Wang, Z.-Y. Pastorello, A. Maeda, K. Reguitti, A. Cai, Y.-Z. Howell, D. Andrew Benetti, S. Buckley, D. Cappellaro, E. Carini, R. Cartier, R. Chen, T.-W. Elias-Rosa, N. Fang, Q.-L. Gal-Yam, A. Gangopadhyay, A. Gromadzki, M. Gan, W.-P. Hiramatsu, D. Hu, M.-K. Inserra, C. McCully, C. Nicholl, M. Olivares, F.E. Pignata, G. Pineda-García, J. Pursiainen, M. Ragosta, F. Rau, A. Roy, R. Sollerman, J. Tartaglia, L. Terreran, G. Valerin, G. Wang, Q. Wang, S.-Q. Young, D.R. Aryan, A. Bronikowski, M. Concepcion, E. Galbany, L. Lin, H. Melandri, A. Petrushevska, T. Ramirez, M. Shi, D.-D. Warwick, B. Zhang, J.-J. Wang, B. Wang, X.-F. Zhu, X.-J. School of Physics and Astronomy Beijing Normal University Beijing100875 China Department of Physics Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China INAF - Osservatorio Astronomico di Padova Vicolo dell’Osservatorio 5 Padova35122 Italy Department of Astronomy Kyoto University Kitashirakawa-Oiwake-cho Sakyo-ku Kyoto606-8502 Japan Merate23807 Italy Yunnan Observatories Chinese Academy of Sciences Kunming650216 China International Centre of Supernovae Yunnan Key Laboratory Kunming650216 China Key Laboratory for the Structure and Evolution of Celestial Objects Chinese Academy of Sciences Kunming650216 China Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117-5575 United States Department of Physics University of California Santa BarbaraCA93106-9530 United States South African Astronomical Observatory PO Box 9 Observatory Cape Town7935 South Africa INAF - Osservatorio Astronomico di Roma via Frascati 33 Monte Porzio CatoneI-00078 Italy Instituto de Estudios Astrofísicos Facultad de Ingeniería y Ciencias Universidad Diego Portales Av. Ejército Libertador 441 Santiago Chile Graduate Institute of Astronomy National Central University 300 Jhongda Road Jhongli32001 Taiwan Campus UAB Carrer de Can Magrans s/n BarcelonaE-08193 Spain Department of Particle Physics and Astrophysics Weizmann Institute of Science Rehovot76100 Israel The Oskar Klein Centre Department of Astronomy Stockholm University AlbaNova StockholmSE-10691 Sweden Astronomical Observatory University of Warsaw Al. Ujazdowskie 4 Warszawa00-478 Poland Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and Technology Guangxi University Nanning530004 China Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138-1516 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States Department of Physics Tsinghua University Beijing10008

We present optical and near-infrared observations of two Type Ibn supernovae (SNe), SN 2018jmt and SN 2019cj. Their light curves have rise times of about 10 days, reaching an absolute peak magnitude of Mg(SN 2018jmt) = −19.07 ± 0.37 and MV(SN 2019cj) = −18.94 ± 0.19 mag, respectively. The early-time spectra of SN 2018jmt are dominated by a blue continuum, accompanied by narrow (600−1000 km s−1) He i lines with P-Cygni profile. At later epochs, the spectra become more similar to those of the prototypical SN Ibn 2006jc. At early phases, the spectra of SN 2019cj show flash ionisation emission lines of C iii, N iii and He ii superposed on a blue continuum. These features disappear after a few days, and then the spectra of SN 2019cj evolve similarly to those of SN 2018jmt. The spectra indicate that the two SNe exploded within a He-rich circumstellar medium (CSM) lost by the progenitors a short time before the explosion. We model the light curves of the two SNe Ibn to constrain the progenitor and the explosion parameters. The ejecta masses are consistent with either that expected for a canonical SN Ib (~ 2 M☉) or those from a massive WR star (> ~ 4 M☉), with the kinetic energy on the order of 1051 erg. The lower limit on the ejecta mass (> ~ 2 M☉) argues against a scenario involving a relatively low-mass progenitor (e.g., MZAMS ~ 10 M☉). We set a conservative upper limit of ~ 0.1 M☉ for the 56Ni masses in both SNe. From the light curve modelling, we determine a two-zone CSM distribution, with an inner, flat CSM component, and an outer CSM with a steeper density profile. The physical properties of SN 2018jmt and SN 2019cj are consistent with those expected from the core collapse of relatively massive, stripped-envelope (SE) stars. © 2024, CC BY.

关键词： Kinetic energy

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Coulomb Engineering of two-dimensional Mott materials

arXiv

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arXiv 2020年

作者： van Loon, Erik G.C.P. Schüler, Malte Springer, Daniel Sangiovanni, Giorgio Tomczak, Jan M. Wehling, Tim O. Institut für Theoretische Physik Universität Bremen Otto-Hahn-Allee 1 Bremen28359 Germany Bremen Center for Computational Materials Science Universität Bremen Am Fallturm 1a Bremen28359 Germany Mathematical Physics Division Department of Physics Lund University Lund Sweden Institute of Solid State Physics TU Wien ViennaA-1040 Austria Institute of Advanced Research in Artificial Intelligence IARAI ViennaA-1030 Austria Institut für Theoretische Physik und Astrophysik and Würzburg-Dresden Cluster of Excellence ct.qmat Universität Würzburg Würzburg97074 Germany I. Institute of Theoretical Physics University of Hamburg HamburgD-22607 Germany The Hamburg Centre for Ultrafast Imaging HamburgD-22761 Germany

Two-dimensional materials can be strongly influenced by their surroundings. A dielectric environment screens and reduces the Coulomb interaction between electrons in the two-dimensional material. Since in Mott materials the Coulomb interaction is responsible for the insulating state, manipulating the dielectric screening provides direct control over Mottness. Our many-body calculations reveal the spectroscopic fingerprints of such Coulomb engineering: we demonstrate eV-scale changes to the position of the Hubbard bands and show a Coulomb engineered insulator-to-metal transition. Based on our proof-of-principle calculations, we discuss the (feasible) conditions under which our scenario of Coulomb engineering of Mott materials can be realized experimentally. Copyright © 2020, The Authors. All rights reserved.

关键词： Dielectric materials

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Associations Between Urinary Metal Levels and Incident Heart Failure: A Multi-Cohort Analysis

引用

JACC. Heart failure 2025年第8期13卷

作者： Irene Martinez-Morata Arce Domingo-Relloso Melanie Mayer Kathrin Schilling Ronald A Glabonjat Katlyn McGraw Tiffany R Sanchez Joel D Kaufman Dhananjay Vaidya Wendy Post Miranda Jones Daichi Shimbo Ying Zhang Amanda M Fretts Gernot Pichler Jason G Umans Jose Manuel Garcia Pinilla Shelley A Cole Juan C Martin-Escudero Josep Redon Maria Grau-Perez R Graham Barr Linda Valeri Steven Shea Maria Tellez-Plaza Richard B Devereux Ana Navas-Acien Department of Environmental Health Sciences Columbia University Mailman School of Public Health New York New York USA. Electronic address: im2557@cumc.columbia.edu. Department of Biostatistics Columbia University Mailman School of Public Health New York New York USA. Department of Environmental Health Sciences Columbia University Mailman School of Public Health New York New York USA. Departments of Environmental and Occupational Health Sciences Medicine and Epidemiology University of Washington Seattle Washington USA. Department of Medicine Johns Hopkins University Baltimore Maryland USA. Department of Medicine Johns Hopkins University Baltimore Maryland USA Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA. Department of Medicine and Department of Epidemiology Columbia University Irving Medical Center New York New York USA. Center for American Indian Health Research Department of Biostatistics and Epidemiology University of Oklahoma Health Sciences Center Oklahoma City Oklahoma USA. Cardiovascular Health Research Unit Department of Epidemiology University of Washington Seattle Washington USA. Department of Cardiology and Karl Landsteiner Institute for Cardiovascular and Critical Care Research Clinic Floridsdorf Vienna Austria. MedStar Health Research Institute and Georgetown-Howard Universities Center for Clinical and Translational Science Washington DC USA. Cardiology Department Hospital Universitario Virgen de la Victoria IBIMA-Plataforma BIONAND Malaga Spain Ciber-Cardiovascular. Instituto de Salud Carlos III Madrid Spain Medicine and Dematology Department University of Malaga Malaga Spain. Population Health Program Texas Biomedical Research Institute San Antonio Texas USA. Medicina Toxicology and Dermatology Departament University of Valladolid Hospital Universitario Rio Hortega Valladolid Spain. Big Data and Artificial Intelligence Unit Biomedical Research Institute INCLIVA Valencia Spain.

BACKGROUND:Environmental metals are recognized cardiovascular disease risk factors, yet the role of metal exposure in heart failure (HF) risk remains understudied. OBJECTIVES:This study aims to evaluate the prospective association of urinary metals with incident HF across 3 geographically and ethnically/racially diverse cohorts: MESA (Multi-Ethnic Study of Atherosclerosis) and SHS (Strong Heart Study) in the United States, and the Hortega Study in Spain. METHODS:Adults 18-85 years of age in MESA (n = 6,601), SHS (n = 2,917), and Hortega (n = 1,300) were followed up to 20 years. Urinary levels of a multi-metal panel were measured at baseline and corrected for urine dilution. Cox proportional hazards and Cox elastic-net models were used to estimate the multi-adjusted (sociodemographic/clinical/lifestyle covariates) HR of incident HF by individual metals and the mixture of 5 metals available in all cohorts, respectively. The pooled HR (95% CI) of HF by 1-unit increase in log2-transformed levels of individual metals (ie, doubling of the dose) across cohorts was estimated using a fixed effects meta-analysis. Analyses by left ventricular ejection fraction were conducted in a subset. RESULTS:A total of 1,001 participants developed HF. In adjusted models, significant associations (pooled HRs [95% CI] per doubling of urinary metal) were identified for cadmium (HR: 1.15 [95% CI: 1.07-1.24]) molybdenum (HR: 1.13 [95% CI: 1.05-1.22]), and zinc (HR: 1.22 [95% CI: 1.14-1.32]). The HRs (95% CIs) for the association of 1 IQR increase in the multi-metal mixture levels and incident HF were 1.38 (95% CI: 1.00-1.86) in MESA, HR: 1.55 (95% CI: 1.28-1.97) in SHS, and HR: 1.08 (95% CI: 0.85-1.63) in Hortega in fully adjusted models. Stratified models by left ventricular ejection fraction were consistent with the pooled results. CONCLUSIONS:Urinary metals are risk factors of HF across 3 diverse populations, supporting the role of reducing metal exposures to lower HF risk.

关键词： biomarkers heart failure meta-analysis metals prevention urinary metals

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Roadmap on Deep Learning for Microscopy

arXiv

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Sparse DNNs with improved adversarial robustness 18

Sparse DNNs with improved adversarial robustness

引用

Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Yiwen Guo Chao Zhang Changshui Zhang Yurong Chen Intel Labs China Academy for Advanced Interdisciplinary Studies Center for Data Science Peking University Institute for Artificial Intelligence Tsinghua University (THUAI) State Key Lab of Intelligent Technologies and Systems Beijing National Research Center for Information Science and Technology (BNRis) Department of Automation Tsinghua University

Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications. By converting dense models into sparse ones, pruning appears to be a promising solution to reducing the computation/memory cost. This paper studies classification models, especially DNN-based ones, to demonstrate that there exists intrinsic relationships between their sparsity and adversarial robustness. Our analyses reveal, both theoretically and empirically, that nonlinear DNN-based classifiers behave differently under l2 attacks from some linear ones. We further demonstrate that an appropriately higher model sparsity implies better robustness of nonlinear DNNs, whereas over-sparsified models can be more difficult to resist adversarial examples.

关键词：

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Classification of Urban Districts based on Road-centric Crowd Movements

Classification of Urban Districts based on Road-centric Crow...

引用

International Symposium on Personal, Indoor, and Mobile Radio Communications

作者： Ryong Lee Jun Lee Kyung-Sook Kim Minwoo Park Sang-hwan Lee Research Data Hub Center Korea Institute of Science and Technology Information Korea Artificial Intelligence Research Center National Institute of Advanced Industrial Science Technology Japan

ISBN: (纸本)9781538660102

Movements of urban citizens largely take part in geo-social urban dynamics, since they exploit diverse urban districts as necessary. However, it is a non-trivial task to measure city-wide crowd movements and analyze them to understand how we exploit a city space for our daily lives. In this paper, we attempt to capture and take advantages of urban crowd movements by exploiting taxis as a sensor capable of monitoring city-wide, continuous, natural and crowd-sourced movements indirectly. Significantly, we propose a road-centric data space model, with which a variety of heterogenous sensor data can be represented in a common form along roads, enabling to hide heterogenous types of primitive movement logs and to support for convenient data integration in terms of roads, time and sensed urban phenomena. Based on the road-centric integration of taxi-based crowd movements, we classify urban districts according to latent temporal road utilization patterns extracted by a Non-negative Matrix Factorization method.

关键词： analyze them understand support

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Cross-cultural validation and measurement invariance of anxiety and depression symptoms: A study of the Brief Symptom Inventory (BSI) in 42 countries

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Journal of affective disorders 2024年 350卷 991-1006页

作者： Gonzalo R Quintana Fernando P Ponce Javier I Escudero-Pastén Juan F Santibáñez-Palma Léna Nagy Mónika Koós Shane W Kraus Zsolt Demetrovics Marc N Potenza Rafael Ballester-Arnal Dominik Batthyány Sophie Bergeron Joël Billieux Peer Briken Julius Burkauskas Georgina Cárdenas-López Joana Carvalho Jesús Castro-Calvo Lijun Chen Giacomo Ciocca Ornella Corazza Rita I Csako David P Fernandez Elaine F Fernandez Hironobu Fujiwara Johannes Fuss Roman Gabrhelík Ateret Gewirtz-Meydan Biljana Gjoneska Mateusz Gola Joshua B Grubbs Hashim T Hashim Md Saiful Islam Mustafa Ismail Martha C Jiménez-Martínez Tanja Jurin Ondrej Kalina Verena Klein András Költő Chih-Ting Lee Sang-Kyu Lee Karol Lewczuk Chung-Ying Lin Christine Lochner Silvia López-Alvarado Kateřina Lukavská Percy Mayta-Tristán Dan J Miller Oľga Orosová Gábor Orosz Gabriel C Quintero Garzola Jano Ramos-Diaz Kévin Rigaud Ann Rousseau Marco De Tubino Scanavino Marion K Schulmeyer Pratap Sharan Mami Shibata Sheikh Shoib Vera Sigre-Leirós Luke Sniewski Ognen Spasovski Vesta Steibliene Dan J Stein Berk C Ünsal Marie-Pier Vaillancourt-Morel Marie Claire Van Hout Beáta Bőthe Departamento de Psicología y Filosofía Facultad de Ciencias Sociales Universidad de Tarapacá Arica Arica y Parinacota Chile. Electronic address: gquintanaz@academicos.uta.cl. Facultad de Psicología Universidad de Talca Chile. Departamento de Psicología y Filosofía Facultad de Ciencias Sociales Universidad de Tarapacá Arica Arica y Parinacota Chile. Doctoral School of Psychology ELTE Eötvös Loránd University Budapest Hungary Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary. Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary. Department of Psychology University of Nevada Las Vegas Las Vegas NV USA. Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary Centre of Excellence in Responsible Gaming University of Gibraltar Gibraltar Gibraltar. Yale University School of Medicine New Haven CT USA Connecticut Council on Problem Gambling Wethersfield CT USA. Departmento de Psicología Básica Clínica y Psicobiología University Jaume I of Castellón Spain. Institute for Behavioural Addictions Sigmund Freud University Vienna Austria. Département de psychologie Université de Montréal Montréal Canada. Institute of Psychology University of Lausanne Lausanne Switzerland Center for Excessive Gambling Addiction Medicine Lausanne University Hospitals (CHUV) Lausanne Switzerland. Institute for Sex Research Sexual Medicine and Forensic Psychiatry University Medical Centre Hamburg-Eppendorf Hamburg Germany. Laboratory of Behavioral Medicine Neuroscience Institute Lithuanian University of Health Sciences Palanga Lithuania. Virtual Teaching and Cyberpsychology Laboratory School of Psychology National Autonomous University of Mexico Mexico. William James Center for Research Departamento de Educação e Psicologia Universidade de Aveiro Aveiro Portugal. Department of Personality Assessment and Psychological Treatments University of Valencia Spain. Department of Psychology College of Humanity and Social Science Fuz

BACKGROUND:Depression and anxiety are among the most prevalent mental health issues experienced worldwide. However, whereas cross-cultural studies utilize psychometrically valid and reliable scales, fewer can meaningfully compare these conditions across different groups. To address this gap, the current study aimed to psychometrically assess the Brief Symptomatology Index (BSI) in 42 countries. METHODS:Using data from the International Sex Survey (N = 82,243; M = 32.39; SD = 12.52; women: n = 46,874; 57 %), we examined the reliability of depression and anxiety symptom scores of the BSI-18, as well as evaluated evidence of construct, invariance, and criterion-related validity in predicting clinically relevant variables across countries, languages, genders, and sexual orientations. RESULTS:Results corroborated an invariant, two-factor structure across all groups tested, exhibiting excellent reliability estimates for both subscales. The 'caseness' criterion effectively discriminated among those at low and high risk of depression and anxiety, yielding differential effects on the clinical criteria examined. LIMITATIONS:The predictive validation was not made against a clinical diagnosis, and the full BSI-18 scale was not examined (excluding the somatization sub-dimension), limiting the validation scope of the BSI-18. Finally, the study was conducted online, mainly by advertisements through social media, ultimately skewing our sample towards women, younger, and highly educated populations. CONCLUSIONS:The results support that the BSI-12 is a valid and reliable assessment tool for assessing depression and anxiety symptoms across countries, languages, genders, and sexual orientations. Further, its caseness criterion can discriminate well between participants at high and low risk of depression and anxiety.

关键词： Anxiety Brief Symptom Inventory Cross-cultural Depression Measurement invariance Psychometric

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NL-Augmenter A Framework for Task-Sensitive Natural Language Augmentation

arXiv

引用

arXiv 2021年

作者： Dhole, Kaustubh D. Gangal, Varun Gehrmann, Sebastian Gupta, Aadesh Li, Zhenhao Mahamood, Saad Mahendiran, Abinaya Mille, Simon Shrivastava, Ashish Tan, Samson Wu, Tongshuang Sohl-Dickstein, Jascha Choi, Jinho D. Hovy, Eduard Dušek, Ondřej Ruder, Sebastian Anand, Sajant Aneja, Nagender Banjade, Rabin Barthe, Lisa Behnke, Hanna Berlot-Attwell, Ian Boyle, Connor Brun, Caroline Sobrevilla Cabezudo, Marco Antonio Cahyawijaya, Samuel Chapuis, Emile Che, Wanxiang Choudhary, Mukund Clauss, Christian Colombo, Pierre Cornell, Filip Dagan, Gautier Das, Mayukh Dixit, Tanay Dopierre, Thomas Dray, Paul-Alexis Dubey, Suchitra Ekeinhor, Tatiana Giovanni, Marco Di Goyal, Tanya Gupta, Rishabh Hamla, Louanes Han, Sang Harel-Canada, Fabrice Honoré, Antoine Jindal, Ishan Joniak, Przemyslaw K. Kleyko, Denis Kovatchev, Venelin Krishna, Kalpesh Kumar, Ashutosh Langer, Stefan Lee, Seungjae Ryan Levinson, Corey James Liang, Hualou Liang, Kaizhao Liu, Zhexiong Lukyanenko, Andrey Marivate, Vukosi de Melo, Gerard Meoni, Simon Meyer, Maxime Mir, Afnan Moosavi, Nafise Sadat Muennighoff, Niklas Hon Mun, Timothy Sum Murray, Kenton Namysl, Marcin Obedkova, Maria Oli, Priti Pasricha, Nivranshu Pfister, Jan Plant, Richard Prabhu, Vinay Pais, Vasile Qin, Libo Raji, Shahab Rajpoot, Pawan Kumar Raunak, Vikas Rinberg, Roy Roberts, Nicholas Rodriguez, Juan Diego Roux, Claude Vasconcellos, P.H.S. Sai, Ananya B. Schmidt, Robin M. Scialom, Thomas Sefara, Tshephisho Shamsi, Saqib N. Shen, Xudong Shi, Yiwen Shi, Haoyue Shvets, Anna Siegel, Nick Sileo, Damien Simon, Jamie Singh, Chandan Sitelew, Roman Soni, Priyank Sorensen, Taylor Soto, William Srivastava, Aman Aditya Srivatsa, K.V. Sun, Tony Mukund Varma, T. Tabassum, A. Tan, Fiona Anting Teehan, Ryan Tiwari, Mo Tolkiehn, Marie Wang, Athena Wang, Zijian Wang, Zijie J. Wang, Gloria Wei, Fuxuan Wilie, Bryan Winata, Genta Indra Wu, Xinyi Wydmanski, Witold Xie, Tianbao Yaseen, Usama Yee, Michael A. Zhang, Jing Zhang, Yue ACKO Agara Amelia R&D New York United States Applied Research Laboratories The University of Texas at Austin United States Bloomberg Brigham Young University United States Carnegie Mellon University United States Center for Data and Computing in Natural Sciences Universität Hamburg Germany Charles River Analytics Charles University Prague Czech Republic Columbia University United States Council for Scientific and Industrial Research DeepMind United Kingdom Department of Computer Science University of Pretoria South Africa Drexel University United States Eberhard Karls University of Tübingen Germany Edinburgh Napier University United Kingdom Emory University United States Fablab by Inetum in Paris France Fraunhofer IAIS Germany Georgia Tech United States Google Brain United States Google Research United States Harbin Institute of Technology China Hasso Plattner Institute University of Potsdam Germany Hong Kong University of Science and Technology Hong Kong IBM Research IIT Delhi India IIT Madras India Illinois Mathematics and Science Academy United States Imperial College London United Kingdom Independent Indian Institute of Science Bangalore India Institut Teknologi Bandung Indonesia Institute of Data Science National University of Singapore Singapore International Institute of Information Technology Hyderabad India Jagiellonian University Poland Jean Monnet University France Johns Hopkins United States KTH Royal Institute of Technology Sweden KU Leuven Belgium MTS AI France Microsoft RedmondWA United States Mphasis NEXT Labs National University of Ireland Galway Ireland National University of Science and Technology Pakistan National University of Singapore Singapore Naver Labs Europe France Peking University China Politecnico di Milano University of Bologna Italy Polytechnic Institute of Paris France Pompeu Fabra University Spain Pontifical Catholic University of Minas Gerais Brazil Princeton University United States Rakuten India India Research Institu

data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its tranformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robutstness analysis results are available publicly on the NL-Augmenter repository (https://***/GEM-benchmark/NL-Augmenter). © 2021, CC BY.

关键词： Python

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