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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary s. Tegenaw, Geletaw s. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver salvado, Olivier Pujol, Oriol sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, s. Kevin Napel, sandy Colantonio, sara Albarqouni, shadi Joshi, smriti Carter, stacy Klein, stefan Petersen, steffen E. Aussó, susanna Awate, suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves saint James salahuddin, Zohaib starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-dept. literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Collective near-field coupling in infrared-phononic metasurfaces for nano-light canalization

arXiv

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arXiv 2020年

作者： Li, Peining Hu, Guangwei Dolado, Irene Tymchenko, Mykhailo Qiu, Cheng-Wei Alfaro-Mozaz, Francisco Javier Casanova, Felix Hueso, Luis E. Liu, song Edgar, James H. Vélez, saül Alu, Andrea Hillenbrand, Rainer Wuhan National Laboratory for Optoelectronics School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China CIC nanoGUNE BRTA Donostia-San Sebastián20018 Spain Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore Advanced Science Research Center City University of New York New York10031 United States IKERBASQUE Basque Foundation for Science Bilbao48013 Spain Tim Taylor Dept. of Chemical Engineering Kansas State University ManhattanKS66506 United States Department of Materials ETH Zürich Zürich8093 Switzerland CIC NanoGUNE BRTA UPV EHU Donostia-San Sebastián20018 Spain

Polaritons - coupled excitations of photons and dipolar matter excitations - can propagate along anisotropic metasurfaces with either hyperbolic or elliptical dispersion. At the transition from hyperbolic to elliptical dispersion (corresponding to a topological transition), various intriguing phenomena are found, such as an enhancement of the photonic density of states, polariton canalization and hyperlensing. Here we investigate theoretically and experimentally the topological transition and the polaritonic coupling of deeply subwavelength elements (structure sizes ∼ λ/100, λ being the free-space wavelength) in a uniaxial infrared-phononic metasurface, a grating of hexagonal boron nitride (hBN) nanoribbons. By hyperspectral infrared nanoimaging, we observe, for the first time, a synthetic transverse optical phonon resonance (that is, the strong collective near-field coupling of the nanoribbons) in the middle of the hBN Reststrahlen band, yielding a topological transition from hyperbolic to elliptical dispersion. We further visualize and characterize the spatial evolution of a deeply subwavelength canalization mode (a spatial confinement of about 300 nm, ∼ λ/22) near the transition frequency, which is a collimated polariton that is the basis for hyperlensing and diffraction-less propagation. Our results provide fundamental insights into the role of polaritonic near-field coupling in metasurfaces for creating topological transitions and polariton canalization. Copyright © 2020, The Authors. All rights reserved.

关键词： Photons

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Considerations and recommendations from the IsMRM Diffusion study Group for preclinical diffusion MRI: Part 3 — Ex vivo imaging: data processing, comparisons with microscopy, and tractography

arXiv

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arXiv 2024年

作者： schilling, Kurt G. Howard, Amy F.D. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, stijn Nasrallah, Fatima syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius sepehrband, Farshid Zimmermann, Jan soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio van de Looij, Yohan Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, shawna Dell' Acqua, Flavio Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao santin, Mathieu D. Grant, samuel C. Obenaus, Andre Kim, Gene s. Wu, Dan Le Bihan, Denis Blackband, stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Res

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (sNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging. © 2024, CC BY.

关键词： Data sharing

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Exploring the effect of image enhancement techniques on COVID‐19 detection using chest X‐rays images

arXiv

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arXiv 2020年

作者： Rahman, Tawsifur Khandakar, Amith Qiblawey, Yazan Tahir, Anas Kiranyaz, serkan Kashem, saad Bin Abul Islam, Mohammad Tariqul Maadeed, somaya Al Zughaier, susu M. Khan, Muhammad salman Chowdhury, Muhammad E.H. Department of Biomedical Physics & Technology University of Dhaka Dhaka 1000 Bangladesh Department of Electrical Engineering Qatar University Doha2713 Qatar Faculty of Robotics and Advanced Computing Qatar Armed Forces‐Academic Bridge Program Qatar Foundation Doha24404 Qatar Dept. of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia BangiSelangor43600 Malaysia Department of Computer Science and Engineering Qatar University Doha2713 Qatar Department of Basic Medical Sciences College of Medicine Biomedical and Pharmaceutical Research Unit QU Health Qatar University Doha2713 Qatar University of Engineering and Technology Peshawar Pakistan

The use of computer-aided diagnosis in the reliable and fast detection of corona virus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. CXR images are sometimes of poor quality and so image enhancement techniques can help the machine learning models to extract valuable discriminating features from the image. Numerous works have been reported on COVID-19 detection from smaller set of original X-ray images. However, the effect of image enhancement in COVID-19 detection was not reported in the literature. This paper explores the effect of various popular image enhancement techniques and states the © 2020, CC BY.

关键词： Image enhancement

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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

arXiv

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arXiv 2022年

作者： Ali, sharib Ghatwary, Noha Jha, Debesh Isik-Polat, Ece Polat, Gorkem Yang, Chen Li, Wuyang Galdran, Adrian Ballester, Miguel-Ángel González Thambawita, Vajira Hicks, steven Poudel, sahadev Lee, sang-Woong Jin, Ziyi Gan, Tianyuan Yu, ChengHui Yan, JiangPeng Yeo, Doyeob Lee, Hyunseok Tomar, Nikhil Kumar Haithmi, Mahmood Ahmed, Amr Riegler, Michael A. Daul, Christian Halvorsen, Pål Rittscher, Jens salem, Osama E. Lamarque, Dominique Cannizzaro, Renato Realdon, stefano de Lange, Thomas East, James E. Institute of Biomedical Engineering Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom Oxford National Institute for Health Research Biomedical Research Centre OxfordOX4 2PG United Kingdom Big Data Institute University of Oxford Li Ka Shing Centre for Health Information and Discovery OxfordOX3 7LF United Kingdom Translational Gastroenterology Unit Nuffield Department of Medicine Experimental Medicine Div. John Radcliffe Hospital University of Oxford OxfordOX3 9DU United Kingdom Computer Engineering Department Arab Academy for Science and Technology Alexandria1029 Egypt SimulaMet Oslo0167 Norway Department of Computer Science UiT The Arctic University of Norway Hansine Hansens veg 18 Tromsø9019 Norway Medical Department Sahlgrenska University Hospital-Mölndal Blå stråket 5 Göteborg413 45 Sweden Veneto Institute of Oncology IOV-IRCCS Via Gattamelata 64 Padua35128 Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Via Franco Gallini 2 PN Aviano33081 Italy Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré 9 Av. Charles de Gaulle Boulogne-Billancourt92100 France Faculty of Medicine University of Alexandria Alexandria21131 Egypt Graduate School of Informatics Middle East Technical University Ankara06800 Turkey City University of Hong Kong Kowloon Hong Kong BCN MedTech Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona08018 Spain Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Automation Tsinghua University Beijing100084 China Smart Sensing & Diagnosis Research Division Korea Atomic Energy Research Institute 34057 Korea Republic of Daegu-Gyeongbuk Medical Innovation Foundation Medical Device Deve

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets. © 2022, CC BY-NC-sA.

关键词： Endoscopy

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TOI-2458 b: A mini-Neptune consistent with in situ hot Jupiter formation

arXiv

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arXiv 2024年

作者： Šubjak, Jan Gandolfi, Davide Goffo, Elisa Rapetti, David Jankowski, Dawid Mizuki, Toshiyuki Dai, Fei serrano, Luisa M. Wilson, Thomas G. Goździewski, Krzysztof Nowak, Grzegorz Jenkins, Jon M. Twicken, Joseph D. Winn, Joshua N. Bieryla, Allyson Ciardi, David R. Cochran, William D. Collins, Karen A. Deeg, Hans J. Garcia, Rafael A. Guenther, Eike W. Hatzes, Artie P. Kabath, Petr Korth, Judith Latham, David W. Livingston, John H. Lund, Michael B. Mathur, savita Narita, Norio Orell-Miquel, Jaume Palle, Enric Persson, Carina M. Redfield, seth schwarz, Richard P. Watanabe, David Ziegler, Carl Astronomical Institute Czech Academy of Sciences Fričova 298 Ondřejov251 65 Czech Republic Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Dipartimento di Fisica Universita degli Studi di Torino via Pietro Giuria 1 TorinoI-10125 Italy Thuringer Landessternwarte Tautenburg Sternwarte 5 Tautenburg07778 Germany NASA Ames Research Center Moffett FieldCA94035 United States Research Institute for Advanced Computer Science Universities Space Research Association WashingtonDC20024 United States Institute of Astronomy Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziadzka 5 Toruń87-100 Poland Astrobiology Center of NINS 2-21-1 Osawa Mitaka Tokyo181-8588 Japan National Astronomical Observatory of Japan 2-21-2 Osawa Mitaka Tokyo181-8588 Japan Institute for Astronomy University of Hawai'i 2680 Woodlawn Drive HonoluluHI96822 United States Department of Physics University of Warwick Gibbet Hill Road CoventryCV4 7AL United Kingdom SETI Institute Mountain ViewCA94043 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States NASA Exoplanet Science Institute-Caltech/IPAC PasadenaCA91125 United States McDonald Observatory The University of Texas AustinTX United States Center for Planetary Systems Habitability The University of Texas AustinTX United States C. Via Lactea S/N Tenerife La LagunaE-38205 Spain Dept. de Astrofisica Tenerife La LagunaE-38206 Spain Universite Paris-Saclay Universite Paris Cite CEA CNRS AIM Gif-sur-Yvette91191 France Lund Observatory Division of Astrophysics Department of Physics Lund University Box 118 Lund22100 Sweden Astronomical Science Program Graduate University for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Komaba Institute for Science The University of Tokyo 3-8-1 Komaba Meguro Tokyo153-8902 Japan Chalmers University of Technology Departmen

We report on the discovery and spectroscopic confirmation of TOI-2458 b, a transiting mini-Neptune around an F-type star leaving the mainsequence with a mass of M*= 1.05 ± 0.03Mo, a radius of R*= 1.31 ± 0.03 Ro, an effective temperature of Teff = 6005 ± 50 K, and a metallicity of -0.10 ± 0.05 dex. By combining TEss photometry with high-resolution spectra acquired with the HARPs spectrograph, we found that the transiting planet has an orbital period of ∼3.74 days, a mass of Mp = 13.31±0.99M⊕ and a radius of Rp = 2.83±0.20 R⊕. The host star TOI-2458 shows a short activity cycle of ∼54 days revealed in the HARPs s-index and Hα times series. We took the opportunity to investigate other F stars showing activity cycle periods comparable to that of TOI-2458 and found that they have shorter rotation periods than would be expected based on the gyrochronology predictions. In addition, we determined TOI-2458's stellar inclination angle to be i*= 10.6+13.3 -10.6 degrees. We discuss that both phenomena (fast stellar rotation and planet orbit inclination) could be explained by in situ formation of a hot Jupiter interior to TOI-2458 b. It is plausible that this hot Jupiter was recently engulfed by the star. Analysis of HARPs spectra has identified the presence of another planet with a period of P = 16.55 ± 0.06 days and a minimum mass of Mp sin i = 10.22 ± 1.90M⊕. Using dynamical stability analysis, we constrained the mass of this planet to the range Mc (10, 25)M⊕. © 2024, CC BY.

关键词： stars

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A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations

arXiv

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arXiv 2024年

作者： Garrucho, Lidia Kushibar, Kaisar Reidel, Claire-Anne Joshi, smriti Osuala, Richard Tsirikoglou, Apostolia Bobowicz, Maciej del Riego, Javier Catanese, Alessandro Gwoździewicz, Katarzyna Cosaka, Maria-Laura Abo-Elhoda, Pasant M. Tantawy, sara W. sakrana, shorouq s. shawky-Abdelfatah, Norhan O. Abdo-salem, Amr Muhammad Kozana, Androniki Divjak, Eugen Ivanac, Gordana Nikiforaki, Katerina Klontzas, Michail E. García-Dosdá, Rosa Gulsun-Akpinar, Meltem Lafcı, Oğuz Mann, Ritse Martín-Isla, Carlos Prior, Fred Marias, Kostas starmans, Martijn P.A. strand, Fredrik Díaz, Oliver Igual, Laura Lekadir, Karim Barcelona Spain Department of Oncology-Pathology Karolinska Institutet Stockholm Sweden Institute of Machine Learning in Biomedical Imaging Helmholtz Center Munich Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany 2nd Dept. of Radiology Medical University of Gdansk Gdansk Poland Parc Taulí Hospital Universitari Sabadell Spain Hospital Germans Trias i Pujol Badalona Spain Centro Mamario Instituto Alexander Fleming Buenos Aires Argentina Department of Diagnostic & Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo Egypt Department of Radiology University Hospital of Heraklion Stavrakia Greece Department of Diagnostic and Interventional Radiology University Hospital Dubrava Zagreb Croatia University of Zagreb School of Medicine Zagreb Croatia Computational BioMedicine Laboratory Institute of Computer Science Foundation for Research and Technology—Hellas Heraklion Greece Department of Radiology School of Medicine University of Crete Heraklion Greece Medical Imaging and Radiology Universitary and Politechnic Hospital La Fe Valencia Spain Department of Radiology Hacettepe University Faculty of Medicine Sihhiye Ankara Turkey Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna Vienna Austria Department of Radiology and Nuclear Medicine Radboud University Medical Center Netherlands University of Arkansas for Medical Sciences Little RockAR United States Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece Heraklion Greece Department of Radiology and Nuclear Medicine Erasmus MC Cancer Institute University Medical Center Rotterdam Rotterdam Netherlands Department of Pathology Erasmus MC Cancer Institute University Medical Center Rotterdam Rotterdam Netherlands Breast Radiology Karolinska University Hospital Stockholm Sweden Passeig Lluís Companys 23 Barcelona Sp

Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pretreatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamic contrast enhanced MRI

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TOI 762 A b AND TIC 46432937 b: TWO GIANT PLANETs TRANsITING M DWARF sTARs

arXiv

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arXiv 2024年

作者： Hartman, Joel D. Bayliss, Daniel Brahm, Rafael Bryant, Edward M. Jordán, Andrés Bakos, Gáspár Á. Hobson, Melissa J. sedaghati, Elyar Bonfils, Xavier Cointepas, Marion Almenara, Jose Manuel Barkaoui, Khalid Timmermans, Mathilde Dransfield, George Ducrot, Elsa Zúñiga-Fernández, sebastián Hooton, Matthew J. Pedersen, Peter Pihlmann Pozuelos, Francisco J. Triaud, Amaury H.M.J. Gillon, Michaël Jehin, Emmanuel Waalkes, William C. Berta-Thompson, Zachory K. Howell, steve B. Furlan, Elise Ricker, George R. Vanderspek, Roland seager, sara Winn, Joshua N. Jenkins, Jon M. Rapetti, David Collins, Karen A. Charbonneau, David Burke, Christopher J. Rodriguez, David R. Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Dept. of Physics University of Warwick Gibbet Hill Road CoventryCV4 7AL United Kingdom Centre for Exoplanets and Habitability University of Warwick Gibbet Hill Road CoventryCV4 7AL United Kingdom Nuncio Monseñor Sótero Sanz 100 Providencia Santiago Chile Facultad de Ingeniería y Ciencias Universidad Adolfo Ibáñez Av. Diagonal las Torres 2640 Peñalolén Santiago Chile Data Observatory Foundation Santiago Chile Mullard Space Science Laboratory University College London Holmbury St Mary DorkingRH5 6NT United Kingdom Observatoire de Genève Département d’Astronomie Université de Genève Chemin Pegasi 51b Versoix1290 Switzerland Av. Alonso de Córdova 3107 Vitacura Santiago763 0355 Chile Univ. Grenoble Alpes CNRS IPAG GrenobleF-38000 France Astrobiology Research Unit Université de Liège Allée du 6 Août 19C LiègeB-4000 Belgium Department of Earth Atmospheric and Planetary Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Calle Vía Láctea s/n Tenerife La Laguna38200 Spain School of Physics & Astronomy University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Paris Region Fellow Marie Sklodowska-Curie Action France AIM CEA CNRS Université Paris-Saclay Université de Paris Gif-sur-YvetteF-91191 France Cavendish Laboratory JJ Thomson Avenue CambridgeCB3 0HE United Kingdom Glorieta de la Astronomía s/n Granada18008 Spain Institute Université de Liège Allée du 6 Août 19C LiègeB-4000 Belgium Department of Physics and Astronomy Dartmouth College HanoverNH03755 United States Department of Astrophysical & Planetary Sciences University of Colorado Boulder BoulderCO80309 United States NASA Ames Research Center Moffett FieldCA94035 United States NASA Exoplanet Science Institute Caltech/IPAC Mail Code 100-22 1200 E. California Blvd. PasadenaCA91125 United States D

We present the discovery of TOI 762 A b and TIC 46432937 b, two giant planets transiting M dwarf stars. Transits of both systems were first detected from observations by the NAsA TEss mission, and the transiting objects are confirmed as planets through high-precision radial velocity (RV) observations carried out with VLT/EsPREssO. TOI 762 A b is a warm sub-saturn with a mass of 0.251 ±0.042 MJ, a radius of 0.744 ±0.017 RJ, and an orbital period of 3.4717 d. It transits a mid-M dwarf star with a mass of 0.442 ± 0.025 M and a radius of 0.4250 ± 0.0091 R. The star TOI 762 A has a resolved binary star companion TOI 762 B that is separated from TOI 762 A by 3′′ . 2 (∼ 319 AU) and has an estimated mass of 0.227 ± 0.010 M. The planet TIC 46432937 b is a warm super-Jupiter with a mass of 3.20 ± 0.11 MJ and radius of 1.188 ± 0.030 RJ. The planet’s orbital period is P = 1.4404 d, and it undergoes grazing transits of its early M dwarf host star, which has a mass of 0.563 ± 0.029 M and a radius of 0.5299 ± 0.0091 R. TIC 46432937 b is one of the highest mass planets found to date transiting an M dwarf star. TIC 46432937 b is also a promising target for atmospheric observations, having the highest Transmission spectroscopy Metric or Emission spectroscopy Metric value of any known warm super-Jupiter (mass greater than 3.0 MJ, equilibrium temperature below 1000 K). © 2024, CC BY.

关键词： Planets

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Event reconstruction using pattern spectra and convolutional neural networks for the Cherenkov Telescope Array 7

Event reconstruction using pattern spectra and convolutional...

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7th Heidelberg International symposium on High-Energy Gamma-Ray Astronomy, Gamma 2022

作者： Wilkinson, M.H.F. Peletier, R.F. Abdalla, H. Abe, H. Abe, s. Abusleme, A. Acero, F. Acharyya, A. Acín Portella, V. Ackley, K. Adam, R. Adams, C. Adhikari, s.s. Aguado-Ruesga, I. Agudo, I. Aguilera, R. Aguirre-santaella, A. Aharonian, F. Alberdi, A. Alfaro, R. Alfaro, J. Alispach, C. Aloisio, R. Alves Batista, R. Amans, J.-P. Amati, L. Amato, E. Ambrogi, L. Ambrosi, G. Ambrosio, M. Ammendola, R. Anderson, J. Anduze, M. Angüner, E.O. Antonelli, L.A. Antonuccio, V. Antoranz, P. Anutarawiramkul, R. Aragunde Gutierrez, J. Aramo, C. Araudo, A. Araya, M. Arbet-Engels, A. Arcaro, C. Arendt, V. Armand, C. Armstrong, T. Arqueros, F. Arrabito, L. Arsioli, B. Artero, M. Asano, K. Ascasíbar, Y. Aschersleben, J. Ashley, M. Attinà, P. Aubert, P. singh, C.B. Baack, D. Babic, A. Backes, M. Baena, V. Bajtlik, s. Baktash, A. Balazs, C. Balbo, M. Ballester, O. Ballet, J. Balmaverde, B. Bamba, A. Bandiera, R. Baquero Larriva, A. Barai, P. Barbier, C. Barbosa Martins, V. Barcelo, M. Barkov, M. Barnard, M. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Bastieri, D. Batista, P.I. Batkovic, I. Bauer, C. Bautista-González, R. Baxter, J. Becciani, U. Becerra González, J. Becherini, Y. Beck, G. Becker Tjus, J. Bednarek, W. Belfiore, A. Bellizzi, L. Belmont, R. Benbow, W. Berge, D. Bernardini, E. Bernardos, M.I. Bernlöhr, K. Berti, A. Berton, M. Bertucci, B. Beshley, V. Bhatt, N. Bhattacharyya, s. Bhattacharyya, W. Bhattacharyya, s. Bi, B. Bicknell, G. Biederbeck, N. Bigongiari, C. Biland, A. Bird, R. Bissaldi, E. Biteau, J. Bitossi, M. Blanch, O. Blank, M. Blazek, J. Bobin, J. Boccato, C. Bocchino, F. Boehm, C. Bohacova, M. Boisson, C. Boix, J. Bolle, J.-P. Bolmont, J. Bonanno, G. Bonavolontà, C. Bonneau Arbeletche, L. Bonnoli, G. Bordas, P. Borkowski, J. Bórquez, s. Bose, R. Bose, D. Bosnjak, Z. Bottacini, E. Böttcher, M. Botticella, M.T. Boutonnet, C. Bouyjou, F. Bozhilov, V. Bozzo, E. Brahimi, L. Braiding, C. Brau-Nogué, s. Breen, s. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen PO Box 407 GroningenNL-9700 AK Netherlands Centre for Space Research North-West University Potchefstroom2520 South Africa Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Pontificia Universidad Católica de Chile Av. Libertador Bernardo O’Higgins 340 Santiago Chile AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité CEA Paris-Saclay IRFU/DAp Bat 709 Orme des Merisiers Gif-sur-Yvette91191 France Centre for Advanced Instrumentation Dept. of Physics Durham University South Road DurhamDH1 3LE United Kingdom Port d’Informació Científica Edifici D Carrer de l’Albareda Cerdanyola del Vallès Bellaterrra08193 Spain School of Physics and Astronomy Monash University MelbourneVIC3800 Australia Laboratoire Leprince-Ringuet École Polytechnique UMR 7638 CNRS IN2P3 Institut Polytechnique de Paris Palaiseau91128 France Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States University of Oslo Department of Physics Sem Saelandsvei 24 - PO Box 1048 Blindern OsloN-0316 Norway EMFTEL department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés08193 Spain Instituto de Física Teórica UAM CSIC Departamento de Física Teórica Universidad Autónoma de Madrid c/ Nicolás Cabrera 13-15 Campus de Cantoblanco UAM Madrid28049 Spain Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin02 Ireland Universidad Nacional Autónoma de México Delegación Coyoacán Ciudad de México04510 Mexico University of Geneva Département de Physique Nucléaire et Corpusculaire 24 rue du Général-Dufour Genève1211 4 Switzerland INFN Dipartimento di Scienze Fisiche e Chimiche U

The Cherenkov Telescope Array (CTA) is the future observatory for ground-based imaging atmospheric Cherenkov telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides camera images that can be used as training data for convolutional neural networks (CNNs) to differentiate signals from background events and to determine the energy of the initial gamma-ray events. Pattern spectra are commonly used tools for image classification and provide the distributions of the sizes and shapes of features comprising an image. The application of pattern spectra on a CNN allows the selection of relevant combinations of features within an image. In this work, we generate pattern spectra from simulated gamma-ray images to train a CNN for signal-background separation and energy reconstruction for CTA. We compare our results to a CNN trained with CTA images and find that the pattern spectra-based analysis is computationally less expensive but not competitive with the purely CTA images-based analysis. Thus, we conclude that the CNN must rely on additional features in the CTA images not captured by the pattern spectra. © Copyright owned by the author(s)

关键词： Cosmology

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The ALMA REBELs survey: Discovery of a massive, highly star-forming and morphologically complex ULIRG at z = 7.31

arXiv

引用

arXiv 2023年

作者： Hygate, A.P.s. Hodge, J.A. da Cunha, E. Rybak, M. schouws, s. Inami, H. stefanon, M. Graziani, L. schneider, R. Dayal, P. Bouwens, R.J. smit, R. Bowler, R.A.A. Endsley, R. Gonzalez, V. Oesch, P.A. stark, D.P. Algera, H.s.B. Aravena, M. Barrufet, L. Ferrara, A. Fudamoto, Y. Hilhorst, J.H.A. De Looze, I. Nanayakkara, T. Pallottini, A. Riechers, D.A. sommovigo, L. Topping, M.W. van der Werf, P. Leiden Observatory Leiden University P.O. Box 9513 Leiden2300 RA Netherlands International Centre for Radio Astronomy Research University of Western Australia 35 Stirling Hwy Crawley26WA 6009 Australia 2601 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Hiroshima Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Departament d’Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 Burjassot ValènciaE-46100 Spain Dipartimento di Fisica Sapienza Università di Roma Piazzale Aldo Moro 5 RomaI-00185 Italy INAF Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 FirenzeI-50125 Italy Sapienza School for Advanced Studies Viale Regina Elena 291 RomaI-00161 Italy INAF/Osservatorio Astronomico di Roma via Frascati 33 Monte Porzio Catone RomaI-00078 Italy Istituto Nazionale di Fisica Nucleare Sezione di Roma1 Piazzale Aldo Moro 2 RomaI-00185 Italy Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy School of Natural Sciences The University of Manchester ManchesterM13 9PL United Kingdom Steward Observatory University of Arizona 933 N Cherry Ave TucsonAZ85721 United States Departmento de Astronomia Universidad de Chile Casilla 36-D Santiago7591245 Chile Camino del Observatorio 1515 Las Condes Santiago7591245 Chile Observatoire de Genève VersoixCH-1290 Switzerland Niels Bohr Institute University of Copenhagen Jagtvej 128 København NDK-2200 Denmark National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo Japan Nucleo de Astronomia Facultad de Ingenieria y Ciencias Universidad Diego Portales Av. Ejercito 441 Santiago Chile Scuola Normale Superiore Piazza dei Cavalieri 7 PisaI- 50126 Italy Waseda Research Institute for Science and Engineering Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo169-8555 Japan Sterrenkun

We present Atacama Large Millimeter/submillimeter Array (ALMA) [Cii] and ∼ 158μm continuum observations of REBELs-25, a massive, morphologically complex ultra-luminous infrared galaxy (ULIRG;LIR = 1.5+−00.85 × 1012 L) at z = 7.31, spectroscopically confirmed by the Reionization Era Bright Emission Line survey (REBELs) ALMA Large Programme. REBELs-25 has a significant stellar mass of M∗ = 8+−42 × 109 M . From dust-continuum and ultraviolet observations, we determine a total obscured + unobscured star formation rate of sFR = 199+−10163 M yr−1. This is about four times the sFR estimated from an extrapolated main-sequence. We also infer a [Cii]-based molecular gas mass of MH2 = 5.1+−52.16 × 1010 M, implying a molecular gas depletion time of tdepl,H2 = 0.3+−00.32 Gyr. We observe a [Cii] velocity gradient consistent with disc rotation, but given the current resolution we cannot rule out a more complex velocity structure such as a merger. The spectrum exhibits excess [Cii] emission at large positive velocities (∼ 500 km s−1), which we interpret as either a merging companion or an outflow. In the outflow scenario, we derive a lower limit of the mass outflow rate of 200 M yr−1, which is consistent with expectations for a star formation-driven outflow. Given its large stellar mass, sFR and molecular gas reservoir ∼ 700 Myr after the Big Bang, we explore the future evolution of REBELs-25. Considering a simple, conservative model assuming an exponentially declining star formation history, constant star formation efficiency, and no additional gas inflow, we find that REBELs-25 has the potential to evolve into a galaxy consistent with the properties of high-mass quiescent galaxies recently observed at z ∼ 4. Copyright © 2023, The Authors. All rights reserved.

关键词： Galaxies

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