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arXiv 2023年

作者： Bozorgpour, Afshin Azad, Bobby Azad, Reza Velichko, Yury Bagci, Ulas Merhof, Dorit Faculty of Informatics and Data Science University of Regensburg Germany South Dakota State University Brookings United States Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Machine and Hybrid Intelligence Lab Northwestern University ChicagoIL United States Fraunhofer Institute for Digital Medicine MEVIS Germany

Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or IVD herniation. We present HCA-Net, a novel contextual attention network architecture for semantic labeling of IVDs, with a special focus on exploiting prior geometric information. Our approach excels at processing features across different scales and effectively consolidating them to capture the intricate spatial relationships within the spinal cord. To achieve this, HCA-Net models IVD labeling as a pose estimation problem, aiming to minimize the discrepancy between each predicted IVD location and its corresponding actual joint location. In addition, we introduce a skeletal loss term to reinforce the model’s geometric dependence on the spine. This loss function is designed to constrain the model’s predictions to a range that matches the general structure of the human vertebral skeleton. As a result, the network learns to reduce the occurrence of false predictions and adaptively improves the accuracy of IVD location estimation. Through extensive experimental evaluation on multi-center spine datasets, our approach consistently outperforms previous state-of-the-art methods on both MRI T1w and T2w modalities. The code-base is accessible to the public on GitHub. © 2023, CC BY.

关键词： Network architecture

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Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review

arXiv

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arXiv 2023年

作者： Azad, Reza Kazerouni, Amirhossein Heidari, Moein Aghdam, Ehsan Khodapanah Molaei, Amirali Jia, Yiwei Jose, Abin Roy, Rijo Merhof, Dorit Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany School of Electrical Engineering Iran University of Science and Technology Tehran Iran Department of Electrical Engineering Shahid Beheshti University Tehran Iran School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in Computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each of these applications, we investigate the novelty, strengths and weaknesses of the different proposed strategies and develop taxonomies highlighting key properties and contributions. Further, if applicable, we outline current benchmarks on different datasets. Finally, we summarize key challenges and discuss different future research directions. In addition, we have provided cited papers with their corresponding implementations in https://***/mindflow-institue/Awesome-Transformer. © 2023, CC BY.

关键词： Deep neural networks

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Interpreting Industrial IoT data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

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IEEE Transactions on Fuzzy Systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big data Monitoring Production Real-time systems

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Comparison of dimensionality reduction techniques on audio signals 20

Comparison of dimensionality reduction techniques on audio s...

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20th Conference Information Technologies - Applicationsand Theory, ITAT 2020

作者： Pál, Tamás Várkonyi, Dániel T. Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Telekom Innovation Laboratories Budapest Hungary

Analysis of audio signals is widely used and very effective technique in several domains like healthcare, transportation, and agriculture. In a general process the output of the feature extraction method results in huge number of relevant features which may be difficult to process. The number of features heavily correlates with the complexity of the following machine learning method. Dimensionality reduction methods have been used successfully in recent times in machine learning to reduce complexity and memory usage and improve speed of following ML algorithms. This paper attempts to compare the state of the art dimensionality reduction techniques as a building block of the general process and analyze the usability of these methods in visualizing large audio datasets. Copyright © 2020 for this paper by its authors.

关键词： Machine learning

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Full-field electroretinography examinations of the human eye with the eye diagnostic device PEP-2000-First results

Full-field electroretinography examinations of the human eye...

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作者： Schieding, Nikola Reuter, Thomas Grundmann, Andreas Walther, Sebastian Klee, Sascha ICM-Institut Chemnitzer Maschinen-und Anlagenbau E.V. Otto-Schmerbach-Str. 19 Chemnitz09117 Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation TU Ilmenau Ilmenau Germany

Full-field electroretinography (full-field ERG) forms the diagnostic basis for numerous pathologies of the eye. For this reason, fast and accurate diagnostics in the field of ophthalmology are essential. Two examination techniques, full-field ERG and pupillometry were combined in a diagnostic device developed by ICM e.V. to reduce the examination process for both examiners and patients. In this paper, the device is examined for the quality of the full-field ERG measurements. A feasibility study with 12 healthy subjects (3 f, 9 m, 36.33 ± 11.94 years) was conducted to evaluate the device. The results showed that the peak times for both light-and dark-adapted measurements were within the range of the researched literature values. However, the amplitudes were markedly lower in both measurements compared to the averaged literature values (dark-adapted about 8.5-fold and light-adapted about 5.5-fold) and are clearly outside the range of values researched. The main reason for this is the use of cup electrodes, which were placed on the skin of the lower eyelid. Nevertheless, plausible and comparable analysis values could be obtained with the eye diagnostic device PEP-2000. Further studies with wire electrodes will be performed. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Ophthalmology

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Influence of electric field orientation on the effect of ocular current stimulation using full field electroretinogram

Influence of electric field orientation on the effect of ocu...

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作者： Blum, Maren-Christina Leydolph, Lilly Klee, Sascha Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Technische Universität Ilmenau Ilmenau Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria

Ocular current stimulation (CS) exhibits potential for the treatment of neurodegenerative ocular diseases. For a full field electroretinogram (ffERG) we found no CS effect on the characteristic waves (a-wave, b'-wave, and b-wave). To investigate whether the orientation of the generated electric field has an influence on the CS effect, this study repeated the previous ffERG study with changing one CS electrode position and compared the results of both studies. In the first study 15 (8 m, 27.5 ± 4.5 years) and in the second study 17 (7 m, 22.0 ± 1.9 years) healthy volunteers were stimulated with an anodal, cathodal, and sham direct CS of 800 μA for 5 min in three sessions (randomized, different days). For both studies, a cut-sized ring rubber electrode was placed around the eye. A square rubber electrode was placed for the first study at the ipsilateral temple and for the second study at the visual cortex. Before (ERG 1) and during (ERG 2) the CS, the ffERG was measured. For both studies, the difference between the ERG 1 and ERG 2 measurement (CS effect) for the three characteristic waves was analyzed and compared between the studies. For statistical analysis, the Mann-Whitney U test with Bonferroni correction was applied (α = 0.05). The comparison of the data distribution showed only slight differences between the studies. The Mann-Whitney U test found no significant difference of the CS effect between the studies for all amplitudes and CS groups (p ≥ 0.0055). In the mean, the latency differences were smaller than the time resolution, therefore no statements for latency effects were possible. It can be concluded that the retinal cells generating the ffERG are not affected by ocular CS in either electrode montages tested. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Rubber

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Technical light-field setup for 3D imaging of the human nerve head validated with an eye model

Technical light-field setup for 3D imaging of the human nerv...

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作者： Schramm, Stefan Dietzel, Alexander Blum, Maren-Christina Link, Dietmar Klee, Sascha Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Technische Universität Ilmenau Ilmenau Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria

With the new technology of 3D light field (LF) imaging, fundus photography can be expanded to provide depth information. This increases the diagnostic possibilities and additionally improves image quality by digitally refocusing. To provide depth information in the human optic nerve head such as in glaucoma diagnostics, a mydriatic fundus camera was upgraded with an LF imager. The aim of the study presented here was the validation of the technical setup and resulting depth estimations with an appropriate eye model. The technical setup consisted of a mydriatic fundus camera (FF450, Carl Zeiss Meditec AG, Jena, Germany) and an LF imager (R12, Raytrix GmbH, Kiel, Germany). The field of view was set to 30°. The eye model (24.65 mm total length) consisted of a two-lens optical system and interchangeable fundus models with papilla excavations from 0.2 to 1 mm in steps of 0.2 mm. They were coated with red acrylic lacquer and vessels were drawn with a thin brush. 15 images were taken for each papilla depth illuminated with green light (wavelength 520 nm ± 20 nm). Papilla depth was measured from the papilla ground to the surrounding flat region. All 15 measurements for each papilla depth were averaged and compared to the printed depth. It was possible to perform 3D fundus imaging in an eye model by means of a novel LF-based optical setup. All LF images could be digitally refocused subsequently. Depth estimation in the eye model was successfully performed over a 30° field of view. The measured virtual depth and the printed model papilla depth is linear correlated. The presented LF setup allowed high-quality 3D one-shot imaging and depth estimation of the optic nerve head in an eye model. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Optical systems

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Formation of Research Area for Malaysian Urban data Centre and Waste Heat Recovery

Formation of Research Area for Malaysian Urban Data Centre a...

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Digital Transformation and Intelligence (ICDI), International Conference on

作者： Marini Othman Siti Sarah Maidin Azizah Suliman Nur Zakri Zakaria Dua' Nassar Abbas M. Al-Ghaili Faculty of Data Science and Information Technology INTI International University Nilai Malaysia Independent Researcher Sepang Malaysia Faculty of Computer & Information Science Princess Nourah bint Abdulrahman University Riyadh KSA Institute of Informatics and Computing in Energy (IICE) Universiti Tenaga Nasional (UNITEN)

ISBN: (纸本)9798350397017

The realization that the energy consumption due to digital application will continue to rise significantly while climate change has persistently demand efficiency in the energy use, has triggered data centers to become more accountable in shouldering the task to operate sustainably. Green strategy for data centers, among others, should look at improving treatment of waste heat generated by their operation and cooling process. While intensive efforts are taking place in Scandinavian countries, many countries are yet to follow in their steps. A case study involving three urban data centres in Malaysia is conducted to gain insight on their potential in implementing waste heat recovery for utilization by relevant systems within their vicinity. It is found that the data centers have attained favourable in term of safety, security and reliability. However, there are a lot to be desired from the aspect of energy efficiency. A way forward suggests for further study to explore adoption and innovation of waste heat recycling models to improve the performance of Malaysian urban data centers in alignment to improve use and reuse of energy whilst making them more climate-neutral.

关键词： data centers Technological innovation Urban areas Water heating Energy efficiency data models Recycling Climate change

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Automated analysis of continuum fields from atomistic simulations using statistical machine learning

arXiv

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arXiv 2022年

作者： Prakash, Arun Sandfeld, S. Institute of Mechancis and Fluid Dynamics Technical University Bergakademie Freiberg Lampadiusstraße 4 Freiberg09599 Germany Institute for Advanced Simulation – IAS-9: Materials Data Science and Informatics Forschungszentrum Juelich GmbH Juelich52425 Germany Chair of Materials Data Science and Materials Informatics Faculty 5 – Georesources and Materials Engineering RWTH Aachen University Aachen52056 Germany

Atomistic simulations of the molecular dynamics/statics kind are regularly used to study small scale plasticity. Contemporary simulations are performed with tens to hundreds of millions of atoms, with snapshots of these configurations written out at regular intervals for further analysis. Continuum scale constitutive models for material behavior can benefit from information on the atomic scale, in particular in terms of the deformation mechanisms, the accommodation of the total strain and partitioning of stress and strain fields in individual grains. In this work we develop a methodology using statistical data mining and machine learning algorithms to automate the analysis of continuum field variables in atomistic simulations. We focus on three important field variables: total strain, elastic strain and microrotation. Our results show that the elastic strain in individual grains exhibits a unimodal log-normal distribution, whilst the total strain and microrotation fields evidence a multimodal distribution. The peaks in the distribution of total strain are identified with a Gaussian mixture model and methods to circumvent overfitting problems are presented. Subsequently, we evaluate the identified peaks in terms of deformation mechanisms in a grain, which e.g., helps to quantify the strain for which individual deformation mechanisms are responsible. The overall statistics of the distributions over all grains are an important input for higher scale models, which ultimately also helps to be able to quantitatively discuss the implications for information transfer to phenomenological models. © 2022, CC BY-NC-SA.

关键词： Machine learning

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From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer

arXiv

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arXiv 2023年

作者： Karim, Md. Rezaul Comet, Lina Molinas Shajalal, Md. Beyan, Oya Deniz Rebholz-Schuhmann, Dietrich Decker, Stefan Information Systems and Databases RWTH Aachen University Germany Department of Data Science and Artificial Intelligence Fraunhofer FIT Germany University of Siegen Germany University of Cologne Faculty of Medicine University Hospital Cologne Institute for Biomedical Informatics Germany ZBMED - Information Center for Life Sciences Germany Faculty of Medicine University of Cologne Germany

Domain experts often rely on most recent knowledge for apprehending and disseminating specific biological processes that help them design strategies for developing prevention and therapeutic decision-making in various disease scenarios. A challenging scenarios for artificial intelligence (AI) is using biomedical data (e.g., texts, imaging, omics, and clinical) to provide diagnosis and treatment recommendations for cancerous conditions. data and knowledge about biomedical entities like cancer, drugs, genes, proteins, and their mechanism is spread across structured (knowledge bases (KBs)) and unstructured (e.g., scientific articles) sources. A large-scale knowledge graph (KG) can be constructed by integrating and extracting facts about semantically interrelated entities and relations. Such a KG not only allows exploration and question answering (QA) but also enables domain experts to deduce new knowledge. However, exploring and querying large-scale KGs is tedious for non-domain users due to their lack of understanding of the data assets and semantic technologies. In this paper, we develop a domain KG to leverage cancer-specific biomarker discovery and interactive QA. For this, we constructed a domain ontology called OncoNet Ontology (ONO), which enables semantic reasoning for validating gene-disease (different types of cancer) relations. The KG is further enriched by harmonizing the ONO, metadata, controlled vocabularies, and biomedical concepts from scientific articles by employing BioBERT- and SciBERT-based information extractors. Further, since the biomedical domain is evolving, where new findings often replace old ones, without having access to up-to-date scientific findings, there is a high chance an AI system exhibits concept drift while providing diagnosis and treatment. Therefore, we finetune the KG using large language models (LLMs) based on more recent articles and KBs. © 2023, CC BY-NC-ND.

关键词： Knowledge graph

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