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arXiv 2022年

作者： Ye, Chen Xie, Xiangnan Lv, Wenxing Huang, Ke Yang, Allen Jian Jiang, Sicong Liu, Xue Zhu, Dapeng Qiu, Xuepeng Tong, Mingyu Zhou, Tong Hsu, Chuang-Han Chang, Guoqing Lin, Hsin Li, Peisen Yang, Kesong Wang, Zhenyu Jiang, Tian Wang, Xiao Renshaw Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link 637371 Singapore State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Physics Laboratory Industrial Training Center Shenzhen Polytechnic Guangdong Shenzhen518055 China Department of NanoEngineering Program of Chemical Engineering University of California La Jolla San DiegoCA92093-0448 United States Program of Materials Science and Engineering University of California La Jolla San DiegoCA92093-0418 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao266000 China Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Insitute of Physics Academia Sinica Taipei115229 Taiwan College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology Academy of Military Sciences PLA China Beijing100010 China Beijing Academy of Quantum Information Sciences Beijing100084 China Beijing Institute for Advanced Study National University of Defense Technology Changsha410073 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Ave 639798 Singapore

MnBi2Te4 (MBT) is the first intrinsic magnetic topological insulator with the interaction of spin-momentum locked surface electrons and intrinsic magnetism, and it exhibits novel magnetic and topological phenomena. Recent studies suggested that the interaction of electrons and magnetism can be affected by the Mn-doped Bi2Te3 phase at the surface due to inevitable structural defects. Here we report an observation of nonreciprocal transport, i.e. current-direction-dependent resistance, in a bilayer composed of antiferromagnetic MBT and nonmagnetic Pt. The emergence of the nonreciprocal response below the Néel temperature confirms a correlation between nonreciprocity and intrinsic magnetism in the surface state of MBT. The angular dependence of the nonreciprocal transport indicates that nonreciprocal response originates from the asymmetry scattering of electrons at the surface of MBT mediated by magnon. Our work provides an insight into nonreciprocity arising from the correlation between magnetism and Dirac surface electrons in intrinsic magnetic topological insulators. © 2022, CC BY.

关键词： Magnetism

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The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

arXiv

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arXiv 2024年

作者： Correia de Verdier, Maria Saluja, Rachit Gagnon, Louis LaBella, Dominic Baid, Ujjwall Tahon, Nourel Hoda Foltyn-Dumitru, Martha Zhang, Jikai Alafif, Maram Baig, Saif Chang, Ken D’Anna, Gennaro Deptula, Lisa Gupta, Diviya Haider, Muhammad Ammar Hussain, Ali Iv, Michael Kontzialis, Marinos Manning, Paul Moodi, Farzan Nunes, Teresa Simon, Aaron Sollmann, Nico Vu, David Adewole, Maruf Albrecht, Jake Anazodo, Udunna Chai, Rongrong Chung, Verena Faghani, Shahriar Farahani, Keyvan Kazerooni, Anahita Fathi Iglesias, Eugenio Kofler, Florian Li, Hongwei Linguraru, Marius George Menze, Bjoern Moawad, Ahmed W. Velichko, Yury Wiestler, Benedikt Altes, Talissa Basavasagar, Patil Bendszus, Martin Brugnara, Gianluca Cho, Jaeyoung Dhemesh, Yaseen Fields, Brandon K.K. Garrett, Filip Gass, Jaime Hadjiiski, Lubomir Hattangadi-Gluth, Jona Hess, Christopher Houk, Jessica L. Isufi, Edvin Layfield, Lester J. Mastorakos, George Mongan, John Nedelec, Pierre Nguyen, Uyen Oliva, Sebastian Pease, Matthew W. Rastogi, Aditya Sinclair, Jason Smith, Robert X. Sugrue, Leo P. Thacker, Jonathan Vidic, Igor Villanueva-Meyer, Javier White, Nathan S. Aboian, Mariam Conte, Gian Marco Dale, Anders Sabuncu, Mert R. Seibert, Tyler M. Weinberg, Brent Abayazeed, Aly Huang, Raymond Turk, Sevcan Rauschecker, Andreas M. Farid, Nikdokht Vollmuth, Philipp Nada, Ayman Bakas, Spyridon Calabrese, Evan Rudie, Jeffrey D. Department of Surgical Sciences Section of Neuroradiology Uppsala University Sweden Department of Radiology University of California San DiegoCA United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine United States Department of Pathology and Laboratory Medicine Indiana Univeristy IN United States Department of Neuroradiology Heidelberg University Germany University of Laval Quebec CityQC Canada Department of Radiation Oncology Duke University DurhamNC United States Radiology Department University of Missouri ColumbiaMO United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Radiology Nassau Medical Center East MeadowNY United States Department of Radiology Stanford University CA United States Neuroimaging Unit ASST Ovest Milanese Legnano Milan Italy Ross University School of Medicine Bridgetown Barbados Department of Radiology University of California Los AngelesCA United States C.M.H. Lahore Medical College Institute of Dentistry Lahore Pakistan Department of Imaging Sciences University of Rochester Medical Center RochesterNY United States Department of Radiology Northwestern University ChicagoIL United States Department of Radiology VA San Diego Medical Center United States School of Medicine Iran University of Medical Sciences Tehran Iran Neuroradiology Department Hospital Garcia de Orta Unidade Local de Saúde de Almada Seixal EPE Almada Portugal Imaging Department Hospital da Luz Lisboa Lisbon Portugal Department of Radiation Oncology University of California IrvineCA United States Department of Diagnostic and Interventional Radiology University Hospital Ulm Ulm Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum Rechts der Isar Technical University of Munich Munich Germany Department of Radiology Scripps Clin

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care. © 2024, CC BY.

关键词： Magnetic resonance imaging

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A strategic review on sustainable approaches in municipal solid waste management andenergy recovery: Role of artificial intelligence,economic stability andlife cycle assessment

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Bioresource technology 2023年 379卷 129044页

作者： Rajendiran Naveenkumar Jayaraj Iyyappan Ravichandran Pravin Seifedine Kadry Jeehoon Han Raveendran Sindhu Mukesh Kumar Awasthi Samuel Lalthazuala Rokhum Gurunathan Baskar Biological Systems Engineering University of Wisconsin-Madison Madison WI 53706 United States Forest Products Laboratory USDA Forest Service Madison WI 53726 United States. Department of Biotechnology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai 602107 India. Department of Biotechnology St. Joseph's College of Engineering Chennai 600119. India. Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon. Department of Chemical Engineering Pohang University of Science and Technology Pohang 37673 Republic of Korea. Department of Food Technology TKM Institute of Technology Kollam Kerala India. College of Natural Resources and Environment Northwest A&F University Yangling Shaanxi Province 712100 China. Department of Chemistry National Institute of Technology Silchar 788010 Assam India. Department of Biotechnology St. Joseph's College of Engineering Chennai 600119. India Department of Applied Data Science Noroff University College Kristiansand Norway. Electronic address: basg2002@***.

The consumption of energy levels has increased in association with economic growth and concurrently increased the energy demand from renewable sources. The need under Sustainable Development Goals (SDG) intends to explore various technological advancements for the utilization of waste to energy. Municipal Solid Waste (MSW) has been reported as constructive feedstock to produce biofuels, biofuel carriers and biochemicals using energy-efficient technologies in risk freeways. The present review contemplates risk assessment and challenges in sorting and transportation of MSW and different aspects of conversion of MSW into energy are critically analysed. The circular bioeconomy of energy production strategies and management of waste are also analysed. The current scenario on MSW and its impacts on the environment are elucidated in conjunction with various policies and amendments equipped for the competent management of MSW in order to fabricate a sustained environment.

关键词： Circular bioeconomy Municipal Solid Waste Risk assessment Sustainable Waste Management

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GPAW: An open Python package for electronic-structure calculations

arXiv

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arXiv 2023年

作者： Mortensen, Jens Jørgen Larsen, Ask Hjorth Kuisma, Mikael Ivanov, Aleksei V. Taghizadeh, Alireza Peterson, Andrew Haldar, Anubhab Dohn, Asmus Ougaard Schäfer, Christian Jónsson, Elvar Örn Hermes, Eric D. Nilsson, Fredrik Andreas Kastlunger, Georg Levi, Gianluca Jónsson, Hannes Häkkinen, Hannu Fojt, Jakub Kangsabanik, Jiban Sødequist, Joachim Lehtomäki, Jouko Heske, Julian Enkovaara, Jussi Winther, Kirsten Trøstrup Dulak, Marcin Melander, Marko M. Ovesen, Martin Louhivuori, Martti Walter, Michael Gjerding, Morten Lopez-Acevedo, Olga Erhart, Paul Warmbier, Robert Würdemann, Rolf Kaappa, Sami Latini, Simone Boland, Tara Maria Bligaard, Thomas Skovhus, Thorbjørn Susi, Toma Maxson, Tristan Rossi, Tuomas Chen, Xi Schmerwitz, Yorick Leonard A. Schiøtz, Jakob Olsen, Thomas Jacobsen, Karsten Wedel Thygesen, Kristian Sommer CAMd Department of Physics Technical University of Denmark Kgs Lyngby2800 Denmark Riverlane Ltd St Andrews House 59 St Andrews Street CambridgeCB2 3BZ United Kingdom School of Engineering Brown University ProvidenceRI United States Department of Electrical and Computer Engineering Boston University United States Department of Physics Technical University of Denmark Lyngby2800 Denmark Science Institute Faculty of Physical Sciences VR-III University of Iceland Reykjavík107 Iceland Department of Physics Chalmers University of Technology GothenburgSE-412 96 Sweden Science Institute Faculty of Physical Sciences University of Iceland VR-III Reykjavík107 Iceland Quantum-Si 29 Business Park Drive BranfordCT06405 United States CatTheory Department of Physics Technical University of Denmark Kgs Lyngby2800 Denmark Departments of Physics and Chemistry Nanoscience Center University of Jyväskylä JyväskyläFI-40014 Finland Department of Applied Physics Aalto University P.O. Box 11100 Aalto00076 Finland CSC – IT Center for Science Ltd. P.O. Box 405 EspooFI-02101 Finland SUNCAT Center for Interface Science and Catalysis SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Department of Chemistry Nanoscience Center University of Jyväskylä JyväskyläFI-40014 Finland University of Freiburg FIT Freiburg Centre for Interactive Materials and Bioinspired Technologies Georges-Köhler-Allee 105 Freiburg79110 Germany Biophysics of Tropical Diseases Max Planck Tandem Group University of Antioquia UdeA Medellin050010 Colombia School of Physics Mandelstam Institute for Theoretical Physics University of the Witwatersrand 1 Jan Smuts Avenue Johannesburg2001 South Africa Freiburger Materialforschungszentrum Universität Freiburg Stefan-Meier-Straße 21 FreiburgD-79104 Germany Computational Physics Laboratory Tampere University P.O. Box 692 TampereFI-33014 Finland Nanomade Department of Physics Technical University of Denmark Kgs Lyngb

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE) providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe–Salpeter Equation (BSE), variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn–Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support of GPU acceleration has been achieved with minor modifications of the GPAW code thanks to the CuPy library. We end the review with an outlook describing some future plans for GPAW. © 2023, CC BY-SA.

关键词： Python

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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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Low frequency non-resonant rectification in spin-diodes

arXiv

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arXiv 2020年

作者： Tomasello, R. Fang, B. Artemchuk, P. Carpentieri, M. Fasano, L. Giordano, A. Prokopenko, O.V. Zeng, Z.M. Finocchio, G. Institute of Applied and Computational Mathematics FORTH Heraklion-CreteGR-70013 Greece Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-Bionics CAS Suzhou Jiangsu215123 China Faculty of RadioPhysics Electronics and Computer Systems Taras Shevchenko National University of Kyiv Kyiv01601 Ukraine Department of Physics Oakland University RochesterMI48309 United States Department of Electrical and Information Engineering Politecnico of Bari via Orabona 4 Bari70125 Italy Italy Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina MessinaI-98166 Italy

Spin-diodes are usually resonant in nature (GHz frequency) and tuneable by magnetic field and bias current with performances, in terms of sensitivity and minimum detectable power, overcoming the semiconductor counterpart, i.e. Schottky diodes. Recently, spin diodes characterized by a low frequency detection (MHz frequency) have been proposed. Here, we show a strategy to design low frequency detectors based on magnetic tunnel junctions having the interfacial perpendicular anisotropy of the same order of the demagnetizing field out-of-plane component. Micromagnetic calculations show that to reach this detection regime a threshold input power has to be overcome and the phase shift between the oscillation magnetoresistive signal and the input radiofrequency current plays the key role in determining the value of the rectification voltage. Copyright © 2020, The Authors. All rights reserved.

关键词： Schottky barrier diodes

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Explainable AI: Definition and attributes of a good explanation for health AI

arXiv

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arXiv 2024年

作者： Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M. Perkins, Zane B. Lagnado, David A. Marsh, William Gimson, Alexander Shafti, Ali Ercole, Ari Banerjee, Amitava Glocker, Ben Schafer, Burkhard Gatsonis, Constantine Grosan, Crina Sent, Danielle Berman, David S. Glass, David O'Regan, Declan P. Letsios, Dimitrios Morrissey, Dylan Pisirir, Erhan Leofante, Francesco Soyel, Hamit Williamson, Jon Grieman, Keri Dube, Kudakwashe Mardsen, Max Nagendran, Myura Tai, Nigel Kostopoulou, Olga Jones, Owain Curzon, Paul Stoner, Rebecca S. Tandle, Sankalp Joshi, Shalmali Mossadegh, Somayyeh Buijsman, Stefan Miller, Tim Madai, Vince Istvan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Nursing Midwifery and Palliative Care King's College London London United Kingdom Centre for Trauma Sciences Blizard Institute Queen Mary University of London London United Kingdom Royal London Hospital Barts Health NHS Trust London United Kingdom Department of Experiment Psychology University College London London United Kingdom Cambridge Liver Unit Addenbrooke's Hospital Cambridge University Hospitals Hills Road Box 210 Cambridge United Kingdom University of Cambridge United Kingdom Cambridge Consultants Ltd. United Kingdom Division of Anaesthesia Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom Magdelene College University of Cambridge United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Computing Imperial College London London United Kingdom SCRIPT Centre for IT and IP Law School of Law University of Edinburgh United Kingdom Center for Statistical Sciences School of Public Health Brown University Providence United States Research Group Artificial Intelligence HU University of Applied Sciences Utrecht Netherlands Jheronimus Academy of Data Science Tilburg University Eindhoven University of Technology s-Hertogenbosch Netherlands Centre for Theoretical Physics Queen Mary University of London London United Kingdom School of Computing Ulster University Belfast United Kingdom MRC Laboratory of Medical Sciences Imperial College London London United Kingdom Department of Informatics King's College London United Kingdom Physiotherapy Department Barts Health NHS Trust London United Kingdom Sport and Exercise Medicine Queen Mary University of London London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Imperial College London United Kingdom Departmen

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. © 2024, CC BY.

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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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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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Experimental Demonstration of Spintronic Broadband Microwave Detectors and Their Capability for Powering Nanodevices

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Physical Review applied 2019年第1期11卷 014022-014022页

作者： Bin Fang Mario Carpentieri Steven Louis Vasyl Tiberkevich Andrei Slavin Ilya N. Krivorotov Riccardo Tomasello Anna Giordano Hongwen Jiang Jialin Cai Yaming Fan Zehong Zhang Baoshun Zhang Jordan A. Katine Kang L. Wang Pedram Khalili Amiri Giovanni Finocchio Zhongming Zeng Key Laboratory of Nanodevices and Applications Suzhou Institute of Nano-tech and Nano-bionics Chinese Academy of Sciences Ruoshui Road 398 Suzhou 215123 P. R. China Department of Electrical and Information Engineering Polytechnic of Bari Bari 70125 Italy Department of Electrical and Computer Engineering Oakland University Rochester Michigan 48309 USA Department of Physics Oakland University Rochester Michigan 48309 USA Department of Physics and Astronomy University of California Irvine California 92697 USA Institute of Applied and Computational Mathematics FORTH GR-70013 Heraklion-Crete Greece Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina 98166 Italy Department of Physics and Astronomy University of California Los Angeles California 90095 USA Western Digital San Jose California 95138 USA Department of Electrical and Computer Engineering University of California Los Angeles California 90095 USA Department of Electrical Engineering and Computer Science Northwestern University Evanston Illinois 60208 USA

Nonlinear dynamics has been the key ingredient to improve the performance, in terms of sensitivity, of biased resonant spintronic diodes beyond their semiconductor counterparts. We experimentally demonstrate a nonlinear regime broadband detection for nanoscale spintronic diodes (NSD) where the rectification properties are independent of the input microwave frequency, and compare the device performance with the state of the art Schottky diode for low-power rectification. This regime is achieved in magnetic tunnel junctions with a canted magnetization of the free layer. We further show that the developed NSD provides sufficient dc voltage to supply a low-power nanodevice − a black phosphorus photosensor. Our results could pave the way for using spintronic detectors as building blocks for self-powered nanosystems, such as implantable biomedical devices, wireless sensors, and portable electronics.

关键词： Magnetization dynamics Optoelectronics Spin transfer torque Spintronics Diodes Energy applications Magnetic tunnel junctions Microwave techniques

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