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arXiv 2021年

作者： Chatterjee, Soumick Nizamani, Faraz Ahmed Nürnberger, Andreas Speck, Oliver Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Institute for Medical Engineering Otto von Guericke University Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany German Center for Neurodegenerative Disease Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successful treatment planning, and magnetic resonance imaging is the principal imaging modality for diagnosing brain tumours and their extent. Deep Learning methods in computer vision applications have shown significant improvement in recent years, most of which can be credited to the fact that a sizeable amount of data is available to train models, and the improvements in the model architectures yield better approximations in a supervised setting. Classifying tumours using such deep learning methods has made significant progress with the availability of open datasets with reliable annotations. Typically those methods are either 3D models, which use 3D volumetric MRIs or even 2D models considering each slice separately. However, by treating one spatial dimension separately or by considering the slices as a sequence of images over time, spatiotemporal models can be employed as "spatiospatial" models for this task. These models have the capabilities of learning specific spatial and temporal relationships while reducing computational costs. This paper uses two spatiotemporal models, ResNet (2+1)D and ResNet Mixed Convolution, to classify different types of brain tumours. It was observed that both these models performed superior to the pure 3D convolutional model, ResNet18. Furthermore, it was also observed that pre-training the models on a different, even unrelated dataset before training them for the task of tumour classification improves the performance. Finally, Pre-trained ResNet Mixed Convolution was observed to be the best model in these experiments, achieving a macro F1-score of 0.9345 and a test accuracy of 96.98%, while at the same time being the model with the least computational cost. Copyright © 2021, The Authors. All r

关键词： Magnetic resonance imaging

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TorchEsegeta: Framework for interpretability and explainability of image-based deep learning models

arXiv

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arXiv 2021年

作者： Chatterjee, Soumick Das, Arnab Mandal, Chirag Mukhopadhyay, Budhaditya Vipinraj, Manish Shukla, Aniruddh Rao, Rajatha Nagaraja Sarasaen, Chompunuch 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 Institute for Medical Engineering Otto von Guericke University Magdeburg Germany German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this paper presents approaches that help to interpret and explain the results of deep learning algorithms by depicting the anatomical areas which influence the decision of the algorithm most. Moreover, this research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models and generate visual interpretations and explanations for clinicians to corroborate their clinical findings. In addition, this will aid in gaining confidence in such methods. The framework builds on existing interpretability and explainability techniques that are currently focusing on classification models, extending them to segmentation tasks. In addition, these methods have been adapted to 3D models for volumetric analysis. The proposed framework provides methods to quantitatively compare visual explanations using infidelity and sensitivity metrics. This framework can be used by data scientists to perform post-hoc interpretations and explanations of their models, develop more explainable tools and present the findings to clinicians to increase their faith in such models. The proposed framework was evaluated based on a use case scenario of vessel segmentation models trained on Time-of-fight (TOF) Magnetic Resonance Angiogram (MRA) images of the human brain. Quantitative and qualitative results of a comparative study of different models and interpretability methods are presented. Furthermore, this paper provides an extensive overview of several existing interpretability and explainability methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance

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On-line parameter tuning for Monte-Carlo tree search in general game playing 30

On-line parameter tuning for Monte-Carlo tree search in gene...

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30th Benelux Conference on Artificial Intelligence, BNAIC 2018

作者： Sironi, Chiara F. Winands, Mark H.M. Games and AI Group Department of Data Science and Knowledge Engineering Maastricht University Netherlands

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Interactive visual labelling versus active learning:an experimental comparison

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Frontiers of Information Technology & Electronic engineering 2020年第4期21卷 524-535页

作者： Mohammad CHEGINI Jurgen BERNARD Jian CUI Fatemeh CHEGINI Alexei SOURIN Keith ANDREWS Tobias SCHRECK Institute of Computer Graphics and Knowledge Visualisation Graz University of TechnologyGraz 8010Austria School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore InfoVis Group University of British ColumbiaVancouver V6T1Z4Canada Max Planck Institute for Meteorology Hamburg 20146Germany Institute of Interactive Systems and Data Science Graz University of TechnologyGraz 8010Austria

Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data *** quality of supervised maching learning depends not only on the type of algorithm used,but also on the quality of the labelled dataset used to train the *** instances in a training dataset is often done manually relying on selections and annotations by expert analysts,and is often a tedious and time-consuming *** learning algorithms can automatically determine a subset of data instances for which labels would provide useful input to the learning *** visual labelling techniques are a promising alternative,providing effective visual overviews from which an analyst can simultaneously explore data records and select items to a *** putting the analyst in the loop,higher accuracy can be achieved in the resulting *** initial results of interactive visual labelling techniques are promising in the sense that user labelling can improve supervised learning,many aspects of these techniques are still largely *** paper presents a study conducted using the mVis tool to compare three interactive visualisations,similarity map,scatterplot matrix(SPLOM),and parallel coordinates,with each other and with active learning for the purpose of labelling a multivariate *** results show that all three interactive visual labelling techniques surpass active learning algorithms in terms of classifier accuracy,and that users subjectively prefer the similarity map over SPLOM and parallel coordinates for *** also employ different labelling strategies depending on the visualisation used.

关键词： Interactive visual labelling Active learning Visual analytics

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Discovering implicational knowledge in wikidata

arXiv

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arXiv 2019年

作者： Hanika, Tom Marx, Maximilian Stumme, Gerd Knowledge and Data Engineering Group University of Kassel Germany ITeG University of Kassel Germany TU Dresden Germany

knowledge graphs have recently become the state-of-The-Art tool for representing the diverse and complex knowledge of the world. Ex-amples include the proprietary knowledge graphs of companies such as Google, Facebook, IBM, or Microsoft, but also freely available ones such as YAGO, DBpedia, and Wikidata. A distinguishing feature of Wikidata is that the knowledge is collaboratively edited and curated. While this greatly enhances the scope of Wikidata, it also makes it impossible for a single individual to grasp complex connections between properties or understand the global impact of edits in the graph. We apply Formal Concept Analysis to efficiently identify comprehensible implications that are implicitly present in the data. Although the complex structure of data modelling inWikidata is not amenable to a direct approach, we overcome this limitation by extracting contextual representations of parts of Wiki-data in a systematic fashion. We demonstrate the practical feasibility of our approach through several experiments and show that the results may lead to the discovery of interesting implicational knowledge. Besides providing a method for obtaining large real-world data sets for FCA, we sketch potential applications in offering semantic assistance for editing and curating Wikidata.68T30 03G10 68T27 Copyright © 2019, The Authors. All rights reserved.

关键词： Formal concept analysis

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Applications of machine learning methods for boiling modeling and prediction: A comprehensive review

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Chemical Thermodynamics and Thermal Analysis 2022年 8卷

作者： Rashidi, M.M. Alhuyi Nazari, M. Harley, C. Momoniat, E. Mahariq, I. Ali, N. Faculty of Science University of Johannesburg PO Box 524 Auckland Park 2006 South Africa Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Sichuan Chengdu 610054 China Faculty of New Sciences and Technologies University of Tehran Tehran Iran Data Science Across Disciplines Research Group Institute for the Future of Knowledge Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa Department of Mathematics and Applied Mathematics University of Johannesburg P.O. Box 524 Auckland Park 2006 South Africa College of Engineering and Technology American University of the Middle East Kuwait

Boiling refers to the heat transfer mechanism that occurs due to the phase transition from liquid to vapor. In comparison with single phase heat transfer, this mechanism has several advantages such as much higher rate at lower temperature differences. Regarding the complexities of two phase heat transfer simulation, due to the involvement of various parameters in this phenomenon, applying intelligent methods such as artificial neural networks could be useful for modeling this type of heat transfer mechanism. In the present article, studies on the modeling of pool boiling heat transfer utilizing machine learning methods have been reviewed, and their findings are reflected. According to the outcomes of the reviewed works, it can be concluded that using intelligent methods can provide accurate predictions of pool boiling heat transfer with a R² of around 0.99 in some cases. In addition, by applying these methods it would be possible to predict the heat transfer in cases of utilizing nanofluids or porous media. The exactness and applicability range of these models is influenced by several elements such as the considered inputs, applied methods and employed functions. Using an appropriate method with optimal parameter values for the relevant intelligent method would lead to higher precision in modeling. © 2022

关键词： Artificial neural network Machine learning Nanofluid Pool boiling

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Senzi: A sentiment analysis lexicon for the latinised Arabic (Arabizi) 12

Senzi: A sentiment analysis lexicon for the latinised Arabic...

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12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019

作者： Tobaili, Taha Fernandez, Miriam Alani, Harith Sharafeddine, Sanaa Hajj, Hazem Glavaš, Goran Knowledge Media Institute Open University United Kingdom Department of Computer Science and Mathematics Lebanese American University Lebanon Department of Electrical and Computer Engineering American University of Beirut Lebanon Data and Web Science Group Universität Mannheim Germany

ISBN: (纸本)9789544520557

Arabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a severe lack of natural language processing (NLP) resources. As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic. In this paper we describe the creation of a sentiment lexicon for Arabizi that was enriched with word embeddings. The result is a new Arabizi lexicon consisting of 11.3K positive and 13.3K negative words. We evaluated this lexicon by classifying the sentiment of Arabizi tweets achieving an F1-score of 0.72. We provide a detailed error analysis to present the challenges that impact the sentiment analysis of Arabizi. © 2019 Association for Computational Linguistics (ACL). All rights reserved.

关键词： Sentiment analysis

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ReconResNet: Regularised residual learning for MR image reconstruction of undersampled cartesian and radial data

arXiv

引用

arXiv 2021年

作者： Chatterjee, Soumick Breitkopf, Mario Sarasaen, Chompunuch Yassin, Hadya Rose, Georg Nürnberger, Andreas Speck, Oliver Faculty of Computer Science Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Otto von Guericke University Magdeburg Biomedical Magnetic Resonance Germany Research Campus Stimulate Otto von Guericke University Magdeburg Germany Institute for Medical Engineering Otto von Guericke University Magdeburg Germany German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

MRI is an inherently slow process, which leads to long scan time for high-resolution imaging. The speed of acquisition can be increased by ignoring parts of the data (undersampling). Consequently, this leads to the degradation of image quality, such as loss of resolution or introduction of image artefacts. This work aims to reconstruct highly undersampled Cartesian or radial MR acquisitions, with better resolution and with less to no artefact compared to conventional techniques like compressed sensing. In recent times, deep learning has emerged as a very important area of research and has shown immense potential in solving inverse problems, e.g. MR image reconstruction. In this paper, a deep learning based MR image reconstruction framework is proposed, which includes a modified regularised version of ResNet as the network backbone to remove artefacts from the undersampled image, followed by data consistency steps that fusions the network output with the data already available from undersampled k-space in order to further improve reconstruction quality. The performance of this framework for various undersampling patterns has also been tested, and it has been observed that the framework is robust to deal with various sampling patterns, even when mixed together while training, and results in very high quality reconstruction, in terms of high SSIM (highest being 0.990±0.006 for acceleration factor of 3.5), while being compared with the fully sampled reconstruction. It has been shown that the proposed framework can successfully reconstruct even for an acceleration factor of 20 for Cartesian (0.968±0.005) and 17 for radially (0.962±0.012) sampled data. Furthermore, it has been shown that the framework preserves brain pathology during reconstruction while being trained on healthy subjects. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge

arXiv

引用

arXiv 2021年

作者： Sarasaen, Chompunuch Chatterjee, Soumick Breitkopf, Mario Rose, Georg Nürnberger, Andreas Speck, Oliver Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Institute for Medical Engineering Otto von Guericke University Magdeburg Germany Research Campus Stimulate 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 German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised. To overcome this spatio-temporal trade-off, this research presents a super-resolution (SR) MRI reconstruction with prior knowledge based fine-tuning to maximise spatial information while reducing the required scan-time for dynamic MRIs. A U-Net based network with perceptual loss is trained on a benchmark dataset and fine-tuned using one subject-specific static high resolution MRI as prior knowledge to obtain high resolution dynamic images during the inference stage. 3D dynamic data for three subjects were acquired with different parameters to test the generalisation capabilities of the network. The method was tested for different levels of in-plane undersampling for dynamic MRI. The reconstructed dynamic SR results after fine-tuning showed higher similarity with the high resolution ground-truth, while quantitatively achieving statistically significant improvement. The average SSIM of the lowest resolution experimented during this research (6.25% of the k-space) before and after fine-tuning were 0.939 ± 0.008 and 0.957 ± 0.006 respectively. This could theoretically result in an acceleration factor of 16, which can potentially be acquired in less than half a second. The proposed approach shows that the super-resolution MRI reconstruction with prior-information can alleviate the spatio-temporal trade-off in dynamic MRI, even for high acceleration factors. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Prominence and dominance in networks 21st

Prominence and dominance in networks

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21st International Conference on knowledge engineering and knowledge Management, EKAW 2018

作者： Schmidt, Andreas Stumme, Gerd Knowledge and Data Engineering Group University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany

ISBN: (纸本)9783030036669

Topographic prominence and dominance were recently developed to quantify the relative importance of mountain peaks. Instead of simply using the height to characterize a mountain, they provide a more meaningful description based on vertical and horizontal distances in the neighborhood. In this paper, we propose structural prominence and dominance for networks, an adaptation of the topographic measures, for the detection of nodes with strong local importance. We create a network "landscape" which is generated by a node’s height and distance to other nodes in the network. We ground our proposed measures on the task of predicting award winners with high and sustainable impact in a co-authorship network. Our experiments show that our measures provide information about a graph, that is not provided by other graph measures. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

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