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arXiv 2022年

作者： Haseljić, Hana Chatterjee, Soumick Frysch, Robert Kulvait, Vojtěch Semshchikov, Vladimir Hensen, Bennet Wacker, Frank Brüsch, Inga Werncke, Thomas Speck, Oliver Nürnberger, Andreas Rose, Georg Institute for Medical Engineering Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Otto von Guericke University Research Campus STIMULATE Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Institute of Materials Physics Helmholtz-Zentrum hereon Geesthacht Germany German Centre for Neurodegenerative Disease Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany Institute of Diagnostic and Interventional Radiology Hannover Medical School Hannover Germany Institute for Laboratory Animal Science Hannover Medical School Hannover Germany

Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-based CBCT data need to be segmented for proper visualisation and interpretation of perfusion maps. This research proposes Turbolift learning, which trains a modified version of the multi-scale Attention UNet on different liver segmentation tasks serially, following the order of the trainings CT, CBCT, CBCT TST - making the previous trainings act as pre-training stages for the subsequent ones - addressing the problem of limited number of datasets for training. For the final task of liver segmentation from CBCT TST, the proposed method achieved an overall Dice scores of 0.874±0.031 and 0.905±0.007 in 6-fold and 4-fold cross-validation experiments, respectively - securing statistically significant improvements over the model, which was trained only for that task. Experiments revealed that Turbolift not only improves the overall performance of the model but also makes it robust against artefacts originating from the embolisation materials and truncation artefacts. Additionally, in-depth analyses confirmed the order of the segmentation tasks. This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases. Copyright © 2022, The Authors. All rights reserved.

关键词： Visualization

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Generative Adversarial Networks for Image Super-Resolution: A Survey

arXiv

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arXiv 2022年

作者： Tian, Chunwei Zhang, Xuanyu Zhu, Qi Zhang, Bob Lin, Jerry Chun-Wei School of Software Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-space-Ground-Ocean Big Data Application Technology 1 Dongxiang Rd Shaanxi Xi’an710129 China School of Software Northwestern Polytechnical University 1 Dongxiang Rd Shaanxi Xi’an710129 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics 101 Section 2 Kuang-Fu Road Jiangsu Nanjing China PAMI Research Group University of Macau 101 Section 2 Kuang-Fu Road Macao999078 China Department of Computer Science Western Norway University of Applied Sciences Bergen Norway

Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR. © 2022, CC BY-NC-ND.

关键词： Generative adversarial networks

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An interview based study of pioneering experiences in teaching and learning complex systems in higher education

arXiv

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arXiv 2018年

作者： Lizier, Joseph T. Harré, Michael S. Mitchell, Melanie DeDeo, Simon Finn, Conor Lindgren, Kristian Lizier, Amanda L. Sayama, Hiroki Centre for Complex Systems Complex Systems Research Group Faculty of Engineering and IT University of Sydney NSW Australia Department of Computer Science Portland State University PortlandOR United States Santa Fe Institute Santa FeNM United States School of Informatics and Computing Indiana University BloomingtonIN United States Department of Social and Decision Sciences Carnegie Mellon University PittsburghPA United States CSIRO Data 61 MarsfieldNSW Australia Department of Space Earth and Environment Chalmers University of Technology Göteborg Sweden School of Education University of Technology Sydney NSW Australia Center for Collective Dynamics of Complex Systems Department of Systems Science and Industrial Engineering Binghamton University State University of New York NY United States

Due to the interdisciplinary nature of complex systems as a field, students studying complex systems at University level have diverse disciplinary backgrounds. This brings challenges (e.g. wide range of computer programming skills) but also opportunities (e.g. facilitating interdisciplinary interactions and projects) for the classroom. However, there is little published regarding how these challenges and opportunities are handled in teaching and learning Complex Systems as an explicit subject in higher education, and how this differs in comparison to other subject areas. We seek to explore these particular challenges and opportunities via an interview-based study of pioneering teachers and learners (conducted amongst the authors) regarding their experiences. We compare and contrast those experiences, and analyse them with respect to the educational literature. Our discussions explored: approaches to curriculum design, how theories/models/frameworks of teaching and learning informed decisions and experience, how diversity in student backgrounds was addressed, and assessment task design. We found a striking level of commonality in the issues expressed as well as the strategies to handle them, for example a significant focus on problem-based learning, and the use of major student-led creative projects for both achieving and assessing learning outcomes. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

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A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction—Application in Fast Biological Spectroscopy

arXiv

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arXiv 2020年

作者： Wang, Zi Guo, Di Tu, Zhangren Huang, Yihui Zhou, Yirong Wang, Jian Feng, Liubin Lin, Donghai You, Yongfu Agback, Tatiana Orekhov, Vladislav Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China School of Computer and Information Engineering Xiamen University of Technology Xiamen China College of Chemistry and Chemical Engineering Key Laboratory for Chemical Biology of Fujian Province High-field NMR Center Xiamen University Xiamen China China Mobile Group Xiamen China Biomedical Intelligent Cloud R&D Center Xiamen University Xiamen China Department of Molecular Sciences Swedish University of Agricultural Sciences Uppsala Sweden Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg Sweden

The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partial sampled exponentials is highly expected in general signal processing and many applications. Deep learning has shown astonishing potential in this field but many existing problems, such as lack of robustness and explainability, greatly limit its applications. In this work, by combining merits of the sparse model-based optimization method and data-driven deep learning, we propose a deep learning architecture for spectra reconstruction from undersampled data, called MoDern. It follows the iterative reconstruction in solving a sparse model to build the neural network and we elaborately design a learnable soft-thresholding to adaptively eliminate the spectrum artifacts introduced by undersampling. Extensive results on both synthetic and biological data show that MoDern enables more robust, high-fidelity, and ultra-fast reconstruction than the state-of-the-art methods. Remarkably, MoDern has a small number of network parameters and is trained on solely synthetic data while generalizing well to biological data in various scenarios. Furthermore, we extend it to an open-access and easy-to-use cloud computing platform (XCloud-MoDern), contributing a promising strategy for further development of biological applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal reconstruction

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COMPARISON OF DOWNHOLE AND SURFACE SAMPLING FOR THE DETERMINATION OF VOLATILE ORGANIC-COMPOUNDS (VOCS) IN GROUND-WATER

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GROUND WATER MONITORING AND REMEDIATION 1992年第1期12卷 126-133页

作者： ROSEN, ME PANKOW, JF GIBS, J IMBRIGIOTTA, TE Michael E. Rosen received his B.A. in chemistry from the State University of New York at Binghamton in 1981 and a Ph.D. in environmental science and engineering from the Oregon Graduate Institute in 1988. His research interests include trace environmental analysis of organic compounds. He is now manager of the Voluntary Cleanup Section of the Oregon Department of Environmental Quality (Environmental Cleanup Division 811 S.W. 6th Ave. Portland OR 97204) where he is working to develop streamlined procedures and processes for the investigation and cleanup of hazardous substance sites. James F. Pankow is professor and chairman of the Department of Environmental Science and Engineering at the Oregon Graduate Institute (Oregon Graduate Institute of Science and Technology Department of Environmental Science and Engineering 19600 N.W. von Neumann Dr. Beaverton OR 97006–1999). He received his B.A. in chemistry in 1973 from the State University of New York in Binghamton and his Ph.D. in environmental engineering science from the California Institute of Technology in Pasadena California in 1979. His group is involved in the study of the physical and chemical processes affecting the behavior of organic and inorganic chemicals in the environment. This work includes the development and application of sensitive analytical methods for the determination of trace organic contaminants in ground water systems. Jacob Gibs is an environmental engineer with the U.S. Geological Survey Water Resources Division (810 Bear Tavern Rd. Ste. 207 West Trenton NJ 08628). He received a B.S. in mechanical engineering in 1968 an M.S. in environmental engineering in 1975 and a Ph.D. in environmental engineering in 1983 all from Drexel University Philadelphia Pennsylvania. He is currently project chief of a study on representative sampling of ground water for trace levels of organic compounds. His research interests include evaluating ground water sampling techniques and devices for purgeable organic compounds design of sampling

The relative precision and accuracy of sampling and analysis methods for the determination of trace concentrations of volatile organic compounds (VOCs) in ground water were compared. Samples were collected from a well containing nanogram-per-liter (ng/L) to microgram-per-liter (mu-g/L) levels of VOCs. A Keck helical rotor submersible pump was used to collect samples at the surface for analysis by purge and trap (P&T) and for analysis by adsorption/thermal desorption (ATD). Downhole samples were collected by passing water through an ATD cartridge. Although slight spontaneous bubble outgassing occurred when the water was brought to the surface, the relative precisions and comparabilities of the surface and downhole methods were generally found to be equivalent from a statistical point of view. A main conclusion of this study is that bringing sample water to the surface for placement in VOC vials (and subsequent analysis by P&T) can be done reliably under many circumstances. However, care must still be taken to prevent adsorption losses and cross contamination. Samples subject to strong bubble outgassing will need to be handled in a special fashion (e.g., by downhole ATD) to minimize volatilization losses. Additionally, the higher sensitivity of the ATD method allows lower detection limits than are possible with P&T. For example, several compounds present at the ng/L level could be determined with confidence by ATD, but not by P&T.

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Application of Graph-Curvature Features in Auxiliary Diagnosis for Histopathological Image Identification of Gastric Cancer

SSRN

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SSRN 2023年

作者： He, Ruilin Li, Chen Yang, Xinyi Yang, Jinzhu Jiang, Tao Grzegorzek, Marcin Sun, Hongzan Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology Chengdu China Institute for Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Shengjing Hospital of China Medical University Shenyang China School of Computer Science and Engineering

Background: Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors, but it has some drawbacks. This paper explores a computer-aided diagnostic method that can identify benign and malignant gastric cancer with histopathology images. Method: This article obtains the most suitable process through multiple experiments, compared multiple methods and features for classification. Firstly, the U-net is applied to segment the image. Next, the nucleus is extracted from the segmented image and the Minimum Spanning Tree (MST) diagram structure is drawn. The third step is to extract the graph-curvature features of histopathology image according to the MST image. Finally, by inputting graph-curvature features into the classifier, the recognition results for benign or malignant can be obtained. Result: During the experiment, we use various methods for comparison. In the image segmentation stage, U-net, watershed algorithm and Otsu threshold segmentation methods are used respectively. Combined with multiple indicators, we find that the U-net method is the most suitable for segmentation of histopathology images. In the feature extraction stage, in addition to extracting graph-edge feature and graph-curvature feature, several basic image features are also extracted, including Red, Green, Blue feature, Gray-Level Co-occurrence Matrix feature, Histogram of Oriented Gradient feature, and Local Binary Pattern feature. In the classifier design stage, we experimented with various methods, such as Support vector machine (SVM), Random forest, Artificial Neural Network, K Nearest Neighbors, VGG-16 and Inception-V3. Through the comparison and analysis, the classification results with an accuracy of 98.57% can be obtained by inputting the graph-curvature feature into SVM classifier. Conclusion: This paper has created a unique feature, graph-curvature feature based on MST to represent and analyze histopathology images. This graph-based feature can be used

关键词： Local binary pattern

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VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

arXiv

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arXiv 2020年

作者： Sekuboyina, Anjany Kumar Husseini, Malek E. Bayat, Amirhossein Löffler, Maximilian Liebl, Hans Li, Hongwei Tetteh, Giles Kukačka, Jan Payer, Christian Štern, Darko Urschler, Martin Chen, Maodong Cheng, Dalong Lessmann, Nikolas Hu, Yujin Wang, Tianfu Yang, Dong Xu, Daguang Ambellan, Felix Amiranashvili, Tamaz Ehlke, Moritz Lamecker, Hans Lehnert, Sebastian Lirio, Marilia de Olaguer, Nicolás Pérez Ramm, Heiko Sahu, Manish Tack, Alexander Zachow, Stefan Jiang, Tao Ma, Xinjun Angerman, Christoph Wang, Xin Brown, Kevin Kirszenberg, Alexandre Puybareau, Élodie Chen, Di Bai, Yiwei Rapazzo, Brandon H. Yeah, Timyoas Zhang, Amber Xu, Shangliang Hou, Feng He, Zhiqiang Zeng, Chan Xiangshang, Zheng Liming, Xu Netherton, Tucker J. Mumme, Raymond P. Court, Laurence E. Huang, Zixun He, Chenhang Wang, Li-Wen Ling, Sai Ho Huynh, Lê Duy Boutry, Nicolas Jakubicek, Roman Chmelik, Jiri Mulay, Supriti Sivaprakasam, Mohanasankar Paetzold, Johannes C. Shit, Suprosanna Ezhov, Ivan Wiestler, Benedikt Glocker, Ben Valentinitsch, Alexander Rempfler, Markus Menze, Björn H. Kirschke, Jan S. Department of Informatics Technical University of Munich Germany Munich School of BioEngineering Technical University of Munich Germany Department of Neuroradiology Klinikum Rechts der Isar Germany Department for Quantitative Biomedicine University of Zurich Switzerland Friedrich Miescher Institute for Biomedical Engineering Switzerland Institute of Biological and Medical Imaging Helmholtz Zentrum München Germany Department of Computing Imperial College London United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Austria Gottfried Schatz Research Center: Biophysics Medical University of Graz Austria School of Computer Science The University of Auckland New Zealand Computer Vision Group iFLYTEK Research South China China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Shenzhen Research Institute of Big Data China School of Biomedical Engineering Health Science Center Shenzhen University China NVIDIA Corporation United States Zuse Institute Berlin Germany 1000shapes GmbH Berlin Germany Damo Academy Alibaba Group China Department of Mathematics University of Innsbruck Austria Department of Electronic Engineering Fudan University China New York University United States France Deep Reasoning AI Inc United States Technical University of Munich Germany East China Normal University China Chinese Academy of Sciences China Lenovo Group China Ping An Technologies China College of Computer Science and Technology Zhejiang University China Real Doctor AI Research Centre Zhejiang University China The University of Texas MD Anderson Cancer Center United States Department of Electronic and Information Engineering The Hong Kong Polytechnic University China Department of Computing The Hong Kong Polytechnic University China The School of Biomedical Engineering University of Technology Sydney Australia Department of Biomedical Engineering Brno University of Technology Czech Republic India

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://***/nqjyw/, https://***/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://***/anjany/verse. © 2020, CC BY-NC-ND.

关键词： computerized tomography

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GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging

arXiv

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arXiv 2024年

作者： Pati, Sarthak Mazurek, Szymon Bakas, Spyridon Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning Indiana University School of Medicine IndianapolisIN United States Medical Working Group MLCommons San FranciscoCA United States AGH University of Krakow Academic Computer Centre Cyfronet Krakow Poland Indiana University Melvin and Bren Simon Comprehensive Cancer Center IndianapolisIN United States Department of Radiology & Imaging Sciences Indiana University School of Medicine IndianapolisIN United States Department of Biostatistics & Health Data Science Indiana University School of Medicine IndianapolisIN United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IndianapolisIN United States

Generative Artificial Intelligence (GenAI) is a field of AI that creates new data samples from existing ones. It utilizing deep learning to overcome the scarcity and regulatory constraints of healthcare data by generating new data points that integrate seamlessly with original datasets. This paper explores the background and motivation for GenAI, and introduces the Generally Nuanced Deep Learning Framework for Synthesis (GaNDLF-Synth) to address a significant gap in the literature and move towards democratizing the implementation and assessment of image synthesis tasks in healthcare. GaNDLF-Synth describes a unified abstraction for various synthesis algorithms, including autoencoders, generative adversarial networks, and diffusion models. Leveraging the GANDLF-core framework, it supports diverse data modalities and distributed computing, ensuring scalability and reproducibility through extensive unit testing. The aim of GaNDLF-Synth is to lower the entry barrier for GenAI, and make it more accessible and extensible by the wider scientific community. © 2024, CC BY.

关键词： Generative adversarial networks

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Review

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Metascience 2002年第1期11卷 43-138页

作者： Craig Callender Steve Fuller Katherine Neal Bernadette Bensaude-Vincent David Turnbull Otávio Bueno Jerry Ravetz Alan Chalmers Peter Harrison Keith Benson Jonathan Simon Margaret D. Garber Samir Chopra Henry Chan Elizabeth Toon Philip Catton Brendan Larvor Kylie Valentine Alan C. Love Donna April Chua Sy Brian W. Ogilvie James Maclaurin William C. Summers Arthur B. Markman John T. Vallance Department of Philosophy University of California San Diego La Jolla USA Department of Sociology University of Warwick Coventry UK Unit for History and Philosophy of Science Sydney University Sydney Australia Département de Philosophie Université Paris X Nanterre France School of Social Inquiry Arts Faculty Deakin University Geelong Australia Department of Philosophy California State University Fresno USA London UK Philosophy Department Flinders University Adelaide Australia Department of Philosophy Bond University Gold Coast Australia University of Washington Seattle USA Max Planck Institute for the History of Science Berlin Germany University of California San Diego La Jolla USA Knowledge Systems Group School of Computer Science and Engineering University of New South Wales Sydney Australia School of Science and Technology Studies University of New South Wales Sydney Australia Science and Technology Studies Cornell University Ithaca USA Department of Philosophy and Religious Studies University of Canterbury Christchurch New Zealand Department of Humanities University of Hertfordshire Hatfield UK Erskineville Australia Department of History and Philosophy of Science University of Pittsburgh Pittsburgh USA Department of Classics University of California Berkeley Berkeley USA Department of History Herter Hall University of Massachusetts Amherst USA Department of Philosophy University of Otago Dunedin New Zealand Yale University School of Medicine New Haven USA Department of Psychology University of Texas Austin USA Sydney Grammar School Darlinghurst Australia

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RETRACTED ARTICLE: Mitigating the Coexistence Technique in Wireless Body Area Networks By Using Superframe Interleaving

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IETE Journal of Research 2023年第11期69卷 CXXXIII-CXLVII页

作者： Samira Kanwal Junaid Rashid Jungeun Kim Sapna Juneja Gaurav Dhiman Amir Hussain 1 Department of Computer Science University of Engineering and Technology Taxila Pakistan 2 Department of Computer Science and Engineering Kongju National University Cheonan31080 Korea 3 KIET Group of Institutions Delhi NCR Ghaziabad India 4 Department of Computer Science Government Bikram College of Commerce Patiala India5 University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan Mohali India6 Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India 7 Centre of AI and Data Science Edinburgh Napier University Edinburgh UK

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”:Samira Kanwal, Junaid Rashid, Jungeun Kim, Sapna Juneja, Gaurav Dhiman & Amir Hussain (2022) Mitigating the Coexistence Technique in Wireless Body Area Networks By Using Superframe Interleaving, IETE Journal of Research, doi:10.1080/03772063.2022.2043788Following publication, concerns were raised about the peer review and decision-making processes for this special *** an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal’s peer review standards and *** the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special *** journal has not confirmed if the authors were aware of this compromised peer review *** journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this *** have been informed in our decision-making by our editorial policies and the COPE *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as “Retracted”.

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