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arXiv 2022年

作者： Chatterjee, Soumick Yassin, Hadya Dubost, Florian Nürnberger, Andreas Speck, Oliver Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Faculty of Nature Sciences Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Institute for Medical Engineering Faculty of Electrical Engineering and Information Technology Otto von Guericke University Magdeburg Germany Department of Biomedical Data Science Stanford University StanfordCA United States Centre for Behavioural Brain Sciences Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany

Deep learning models have shown their potential in recent years for several applications. However, most of the models are opaque in nature and difficult to trust due to their complex reasoning - commonly known as the black-box problem. Some fields, such as medicine, require a high degree of transparency to accept and adopt such technologies. Consequently, there is a movement towards creating explainable/interpretable models or applying post-hoc explainability/interpretability methods on classifiers to build trust in deep learning models. Moreover, deep learning methods can be used for segmentation tasks, which typically require hard-to-obtain, time-consuming manually-annotated segmentation labels for training. This paper introduces three inherently-explainable classifiers to tackle both of these problems as one. The localisation heatmaps provided by the networks - representing the models' focus areas and being used in classification decision-making - can be directly interpreted, without requiring any post-hoc methods to derive information for model explanation. The models are trained by using the input image and only the classification labels as groundtruth in a supervised fashion - without using any information about the location of the region of interest (i.e. the segmentation labels), making the segmentation training of the models weakly-supervised through classification labels. The final segmentation of the regions of interest is obtained by thresholding these heatmaps. The models were employed for the task of multi-class brain tumour classification using two different datasets, resulting in the best F1-score of 0.93 for the supervised classification task while securing a median Dice score of 0.67 ± 0.08 for the weakly-supervised segmentation task. Furthermore, the obtained accuracy on a subset of tumour-only images outperformed the state-of-the-art glioma tumour grading binary classifiers with the best model achieving 98.7% accuracy. Copyright © 2022, The Autho

关键词： Magnetic resonance imaging

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Complex-valued neural networks to speed-up MR Thermometry during Hyperthermia using Fourier PD and PDUNet

arXiv

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arXiv 2023年

作者： Khatun, Rupali Chatterjee, Soumick Bert, Christoph Wadepohl, Martin Ott, Oliver J. Semrau, Sabine Fietkau, Rainer Nürnberger, Andreas Gaipl, Udo S. Frey, Benjamin Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany Comprehensive Cancer Centre Erlangen-EMN Erlangen Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Dr Sennewald Medizintechnik GmbH Munich Germany

Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously heated to temperatures between 39 and 43 °C for 60 minutes. Temperature monitoring can be performed non-invasively using dynamic magnetic resonance imaging (MRI). However, the slow nature of MRI leads to motion artefacts in the images due to the movements of patients during image acquisition. By discarding parts of the data, the speed of the acquisition can be increased - known as undersampling. However, due to the invalidation of the Nyquist criterion, the acquired images might be blurry and can also produce aliasing artefacts. The aim of this work was, therefore, to reconstruct highly undersampled MR thermometry acquisitions with better resolution and with fewer artefacts compared to conventional methods. The use of deep learning in the medical field has emerged in recent times, and various studies have shown that deep learning has the potential to solve inverse problems such as MR image reconstruction. However, most of the published work only focuses on the magnitude images, while the phase images are ignored, which are fundamental requirements for MR thermometry. This work, for the first time, presents deep learning-based solutions for reconstructing undersampled MR thermometry data. Two different deep learning models have been employed here, the Fourier Primal-Dual network and the Fourier Primal-Dual UNet, to reconstruct highly undersampled complex images of MR thermometry. MR images of 44 patients with different sarcoma types who received HT treatment in combination with radiotherapy and/or chemotherapy were used in this study. The method reduced the temperature difference between the undersampled MRIs and the fully sampled MRIs from 1.3 °C to 0.6 °C in full volume and 0.49 °C to 0.06 °C in the tumour region for an acceleration factor of 10. Copyright © 2023, The Authors. All rights res

关键词： Chemotherapy

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Marginal energy consumption cost factors of battery electric buses

Marginal energy consumption cost factors of battery electric...

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Smart Cities Symposium Prague (SCSP)

作者： Pé ter Á kos Szilassy Bence Jenő fi Dá vid Fö ldes PhD Student BME-KTKG and Transport Engineer NATUK Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Hungary Hungarian National Data Economy Knowledge Centre (Neumann J&#x00E1 nos Nonprofit Ltd. - NATUK) Budapest Business Analyst NATUK nos Nonprofit Ltd. - NATUK) Budapest Hungary Research Associate BME-KTKG Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Budapest Hungary

Regulatory environment in the EU already mandates public transport providers (PTP) to detail their cost accounting to the level of each line they operate. Models used, however, fail to utilise the level of precision state of the art technology would allow for; therefore, state-owned providers become uncompetitive. Implementing new technologies such as electric buses have high investment costs; thus, reconsidering the cost structure of an operator is essential. The aim of this paper is to introduce an innovative cost calculation method for battery electric bus operation. The novelty of the model is its foundation on telemetric data generated by sensors hosted in electric buses and the consequent use of marginal consumption costs instead of averages based on aggregations. The model introduced focuses on the cost of energy consumption, examines influencing factors, such as the effect of meteorological externalities on auxiliary systems, and considers driving dynamics. Nonetheless, fix costs such as amortisation and labour costs are also accounted for to give a complete picture of per passenger numbers. The model developed was applied as a case study to prove its applicability. We found that the climbed elevation positively correlates, while the number of stops served at a given distance negatively correlates with the increase in consumption. The model is better suited for improving the operative competitiveness of PTPs rather than as a tool for regulatory compliance.

关键词： Energy consumption Costs Smart cities Biological system modeling Roads Batteries Sensors

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Global Collaboration to Enhance Information Technology & Quantitative Management: Preface for ITQM 2023

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Procedia Computer Science 2023年 221卷 xv-xvii页

作者： Shi, Yong Filip, Florin G. He, Jing Li, Jianping Kou, Gang Tien, James Berg, Daniel Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Romanian Academy Calea Victoriei 125 Sector 1 Bucharest010071 Romania College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States University of Oxford United Kingdom School of Business Chengdu University Sichuan 610106 China College of Engineering University of Miami Coral GablesFL33124 United States

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Efficient Contact Tracing for pandemics using blockchain

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Informatics in Medicine Unlocked 2021年 26卷

作者： Bari, Nida Qamar, Usman Khalid, Ayesha Knowledge & Data Science Research Centre Computer & Software Engineering Department College of Electrical and Mechanical Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan The Centre for Secure Information Technologies (CSIT) ECIT Institute Queen's University of Belfast Belfast BT7 1NN United Kingdom

Blockchain technologies have been benefiting many industries by being decentralized, secure and confidential. They offer great potential in pandemic impacted scenarios as well. Contact tracing helps to mitigate the transmission of disease by alerting people who may have been exposed so they can act on time to protect themselves. Contact tracing systems face some challenges related to issues of medical privacy, data security and transparency. Multiple researches show concern that contact tracing discourages people to seek medication because of the fear of loss of data, subsequent stigma, discrimination, or abuse. In this paper, we discuss how contact tracing can be improved using blockchain technology and could be able to solve these issues. The aim of our proposed system would be to reduce the impact of pandemic, to implement this merger of the blockchains and the Contact tracing app and to ensure user privacy and avoid data misuse by incorporating a symmetric key cryptographic mechanism. To tackle the scalability related issues of Blockchain this framework uses IPFS, a distributed file storage system also known as Interplanetary File System. This blockchain based solution system will enhance contact tracing by making it more stable, secure, performant, highly useable and above all effective in the fight against any pandemic. © 2021 The Authors

关键词： And pandemic Blockchain Contact tracing COVID-19 Distributed system Interplanetary file system

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Assessing the quality of R2RML mappings 1

Assessing the quality of R2RML mappings

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Joint 1st International Workshop on Semantics for Transport and the 1st International Workshop on Approaches for Making data Interoperable, SEM4TRA-AMAR 2019

作者： Crotti, Ademar Debattista, Jeremy O’Sullivan, Declan ADAPT Centre for Digital Content Platform Research Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland

This paper presents an approach to assess the quality of mappings used to generate RDF datasets. data quality is a multidimensional concept determined by many factors which influence the extent by which a dataset is useful for a particular task. Several solutions have been proposed in literature to assess the quality of RDF datasets. Nonetheless, in most cases, these solutions focus on the resulting datasets and not on the artefacts used to generate these. In this paper, we propose the use of metrics commonly used to assess the quality of such datasets to evaluate the mappings used to generate them. The goal is to assist data providers into producing high quality datasets by bringing such quality assessment procedures to also cover the start of the publishing process. We provide an implementation of the approach by extending an existing quality assessment framework, which is then evaluated using real world use cases. Preliminary results shows that the assessment of mappings is capable to identifying quality issues for the observed cases. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Mapping

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Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging

arXiv

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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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Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction

arXiv

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arXiv 2021年

作者： Ernst, Philipp Chatterjee, Soumick Rose, Georg Speck, Oliver Nürnberger, Andreas 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 for Medical Engineering Otto von Guericke University Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany

Computed tomography (CT) and magnetic resonance imaging (MRI) are two widely used clinical imaging modalities for noninvasive diagnosis. However, both of these modalities come with certain problems. CT uses harmful ionising radiation, and MRI suffers from slow acquisition speed. Both problems can be tackled by undersampling, such as sparse sampling. However, such undersampled data leads to lower resolution and introduces artefacts. Several techniques, including deep learning based methods, have been proposed to reconstruct such data. However, the undersampled reconstruction problem for these two modalities was always considered as two different problems and tackled separately by different research works. This paper proposes a unified solution for both sparse CT and undersampled radial MRI reconstruction, achieved by applying Fourier transform-based pre-processing on the radial MRI and then finally reconstructing both modalities using sinogram upsampling combined with filtered back-projection. The Primal-Dual network is a deep learning based method for reconstructing sparsely-sampled CT data. This paper introduces Primal-Dual UNet, which improves the Primal-Dual network in terms of accuracy and reconstruction speed. The proposed method resulted in an average SSIM of 0.932±0.021 while performing sparse CT reconstruction for fan-beam geometry with a sparsity level of 16, achieving a statistically significant improvement over the previous model, which resulted in 0.919±0.016. Furthermore, the proposed model resulted in 0.903±0.019 and 0.957±0.023 average SSIM while reconstructing undersampled brain and abdominal MRI data with an acceleration factor of 16, respectively-statistically significant improvements over the original model, which resulted in 0.867±0.025 and 0.949±0.025. Finally, this paper shows that the proposed network not only improves the overall image quality, but also improves the image quality for the regions-of-interest: liver, kidneys, and spleen;as well

关键词： Image reconstruction

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Cross-cohort analysis identifies shared gut microbial signatures and validates microbial risk scores for colorectal cancer

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Journal of translational medicine 2025年第1期23卷 676页

作者： Yuhan Zhang Jiahui Luo Kuangyu Chen Na Li Chenyu Luo Shuang Di Junjie Qin Feng Zhang Hongda Chen Min Dai Center for Clinical and Epidemiologic Research Beijing Anzhen Hospital Capital Medical University Beijing Institute of Heart Lung and Blood Vessel Diseases Beijing 100029 China. Department of Cancer Epidemiology National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 100021 China. Center for Prevention and Early Intervention National Infrastructures for Translational Medicine Institute of Clinical Medicine Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 100730 China. Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Beijing 100872 China. Centre for Data Science and Digital Health Hamilton Health Sciences Hamilton ON L8P 0A1 Canada. Dalla Lana School of Public Health University of Toronto Toronto ON M5T 3M7 Canada. School of Life Science and Technology School of Food Science and Engineering Nutrition and Health Research Institute Wuhan Polytechnic University Wuhan 430024 China. Center for Prevention and Early Intervention National Infrastructures for Translational Medicine Institute of Clinical Medicine Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 100730 China. chenhongda@***. Department of Cancer Epidemiology National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 100021 China. daimin2002@***.

BACKGROUND:Microbiome-wide association studies showed links between colorectal cancer (CRC) and gut microbiota. However, the clinical application of gut microbiota in CRC prevention has been hindered by the diversity of study populations and technical variations. We aimed to determine CRC-related gut microbial signatures based on cross-regional, cross-population, and cross-cohort metagenomic datasets, and elucidate its application value in CRC risk assessment. METHODS:We used the MMUPHin tool to perform a meta-analysis of our own cohort and seven publicly available metagenomics datasets to identify gut microbial species associated with CRC across different cohorts, comprising of 570 CRC cases and 557 controls. Based on differential species sets, we constructed the microbial risk score (MRS) using α-diversity of the sub-community (MRS), weighted/unweighted summation methods and machine learning algorithms. Cohort-to-cohort training and validation were performed to demonstrate the transferability. RESULTS:We found that MRS of core species was better validated and more interpretable than those constructed with summation methods or machine learning algorithms. Six species, including Parvimonas micra, Clostridium symbiosum, Peptostreptococcus stomatis, Bacteroides fragilis, Gemella morbillorum, and Fusobacterium nucleatum, were included in MRS constructed by half or more of the cohorts. The AUC of MRS, calculated based on the sub-community of six species, varied between 0.619 and 0.824 across the eight cohorts. CONCLUSION:We identified six CRC-related species across regions, populations, and cohorts. The constructed MRS could contribute to the risk prediction of CRC in different populations.

关键词： Colorectal cancer Cross-cohort Disease prediction Gut microbiota Microbial risk score

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SMILE-UHURA Challenge - Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms

arXiv

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arXiv 2024年

作者： Chatterjee, Soumick Mattern, Hendrik Dörner, Marc Sciarra, Alessandro Dubost, Florian Schnurre, Hannes Khatun, Rupali Yu, Chun-Chih Hsieh, Tsung-Lin Tsai, Yi-Shan Fang, Yi-Zeng Yang, Yung-Ching Huang, Juinn-Dar Xu, Marshall Liu, Siyu Ribeiro, Fernanda L. Bollmann, Saskia Chintalapati, Karthikesh Varma Radhakrishna, Chethan Mysuru Ram Kumar, Sri Chandana Hudukula Sutrave, Raviteja Qayyum, Abdul Mazher, Moona Razzak, Imran Rodero, Cristobal Niederer, Steven Lin, Fengming Xia, Yan Wang, Jiacheng Qiu, Riyu Wang, Liansheng Panah, Arya Yazdan Jurdi, Rosana El Fu, Guanghui Arslan, Janan Vaillant, Ghislain Valabregue, Romain Dormont, Didier Stankoff, Bruno Colliot, Olivier Vargas, Luisa Chacón, Isai Daniel Pitsiorlas, Ioannis Arbeláez, Pablo Zuluaga, Maria A. Schreiber, Stefanie Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Magdeburg Germany Department of Neurology Medical Faculty University Hospital of Magdeburg Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany University Hospital Zurich University of Zurich Zurich Switzerland Google Inc. United States Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany National Yang Ming Chiao Tung University Hsinchu Taiwan School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia Australian eHealth Research Centre Chile National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates The Alan Turing Institute London United Kingdom School of Computing University of Leeds Leeds United Kingdom Department of Computer Science School of Informatics Xiamen University Xiamen China Manteia Technologies Co. Ltd Xiamen China Leicester International Institute Dalian University of Technology Dalian China Sorbonne Université Institut du Cerveau - Paris Brain Institute ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié Salpêtrière ParisF-75013 France Centre of Formation and Research in Artificial Intelligence Universidad de Los Andes Colombia Data Science Department EURECOM Sophia Antipolis Franc

The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulnerability within the cerebral blood supply and can lead to severe conditions, such as Cerebral Small Vessel Diseases. The advent of 7 Tesla MRI systems has enabled the acquisition of higher spatial resolution images, making it possible to visualise such vessels in the brain. However, the lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. To address the complexities of mesoscopic vessel segmentation and to highlight the need for advanced techniques to manage the high noise levels and poor vessel-to-background contrast inherent in"ultra-high-resolution" data, the SMILE-UHURA challenge was organised. This challenge, held in conjunction with the ISBI 2023, in Cartagena de Indias, Colombia, aimed to provide a platform for researchers working on related topics. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. This dataset was created through a combination of automated pre-segmentation and extensive manual refinement. In this manuscript, sixteen submitted methods and two baseline methods are compared both quantitatively and qualitatively on two different datasets: held-out test MRAs from the same dataset as the training data (with labels kept secret) and a separate 7T ToF MRA dataset where both input volumes and labels are kept secret. The results demonstrate that most of the submitted deep learning methods, trained on the provided training dataset, achieved reliable segmentation performance. Dice scores reached up to 0.838 ± 0.066 and 0.716 ± 0.125 on the respective datasets, with an average performance of up to 0.804 ± 0.15. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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