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arXiv 2023年

作者： Chatterjee, Soumick Gaidzik, Franziska Sciarra, Alessandro Mattern, Hendrik Janiga, Gábor Speck, Oliver Nürnberger, Andreas Pathiraja, Sahani Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Laboratory of Fluid Dynamics and Technical Flows Otto von Guericke University Magdeburg Germany Department of Biomedical Magnetic Resonance Faculty of Natural Sciences Otto von Guericke University Magdeburg Germany Department of Neurology Medical Faculty University Hospital Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany School of Mathematics & Statistics UNSW Sydney UNSW Data Science Hub Australia

In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets. These issues may result in segmentations that lack the requisite precision for clinical analysis and can be misleadingly overconfident without associated uncertainty quantification. We propose the PULASki as a computationally efficient generative tool for biomedical image segmentation that accurately captures variability in expert annotations, even in small datasets. Our approach makes use of an improved loss function based on statistical distances in a conditional variational autoencoder structure (Probabilistic UNet), which improves learning of the conditional decoder compared to the standard cross-entropy particularly in class imbalanced problems. We analyse our method for two structurally different segmentation tasks (intracranial vessel and multiple sclerosis (MS) lesion) and compare our results to four well-established baselines in terms of quantitative metrics and qualitative output. These experiments are characterised by class-imbalanced, low signal to noise ratio data sets with highly ambiguous features. Empirical results demonstrate the PULASKi method outperforms all baselines at the 5% significance level. Our experiments are also of the first to present a comparative study of the computationally feasible segmentation of complex geometries using 3D patches and the traditional use of 2D slices. The generated segmentations are shown to be much more anatomically plausible than in the 2D case, particularly for the vessel task. Our method can also be applied to a wide range of multi-label segmentation tasks and and is useful for downstream tasks such as hemodynamic modelling (computational fluid dynamics and data assimilation), clinical decision making, and treatment planning. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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Raman scattering investigation of a Ge/SiO2/Si nanocrystal system under hydrostatic pressure

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Physical Review B 2004年第12期69卷 125333-125333页

作者： Lei Liu K. L. Teo Z. X. Shen J. S. Sun E. H. Ong A. V. Kolobov Y. Maeda Physics Department Blk. S12 Faculty of Science National University of Singapore 2 Science Drive 3 117542 Singapore Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 117576 Singapore Mechatronics & Micro System Group Data Storage Institute 117608 Singapore Center for Applied Near-Field Optics Research (CANFOR) National Institute of Advanced Industrial Science and Technology Tsukuba Central 4 1-1-1 Higashi Tsukuba Ibaraki 305-8562 Japan Department of Energy Science and Technology Kyoto University Sakyo-ku Kyoto 606-8501 Japan

We have studied the hydrostatic pressure dependence of Ge nanocrystals embedded in a SiO2 matrix on a Si substrate by Raman scattering at room temperature. During the first cycle of increasing pressure, we observed a step change in the Ge Raman mode from 310.4 to 313.8cm−1 at ∼23kbar. The linear pressure coefficients α obtained before and after the step change at ∼23kbar are 0.42 and 0.64cm−1kbar−1, respectively. Upon decreasing pressure, the Ge mode follows a single slope of pressure coefficient α=0.64cm−1kbar−1. A finite-element analysis was carried out to investigate the elastic-field distribution in the Ge/SiO2/Si nanocrystal system, where the discontinuity of the specific geometric configuration with different elastic constants causes local areas of stress concentration around the interface. The step change of the Raman shifts with pressure at ∼23kbar was attributed to complete delamination between the SiO2 film and the Si substrate.

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2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG data Sets 35

2021 BEETL Competition: Advancing Transfer Learning for Subj...

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35th Conference on Neural Information Processing Systems - Competitions and Demonstrations, NeurIPS 2021

作者： Wei, Xiaoxi Aldo Faisal, A. Grosse-Wentrup, Moritz Gramfort, Alexandre Chevallier, Sylvain Jayaram, Vinay Jeunet, Camille Bakas, Stylianos Ludwig, Siegfried Barmpas, Konstantinos Bahri, Mehdi Panagakis, Yannis Laskaris, Nikolaos Adamos, Dimitrios A. Zafeiriou, Stefanos Duong, William C. Gordon, Stephen M. Lawhern, Vernon J. Śliwowski, Maciej Rouanne, Vincent Tempczyk, Piotr Brain and Behaviour Lab Imperial College London United Kingdom Institute of Artificial and Human Intelligence University of Bayreuth Germany Faculty of Computer Science CogSciHub Data Science@Univie University of Vienna Austria Universite Paris-Saclay Inria CEA Palaiseau France LISV UVSQ Université Paris-Saclay France Reality Labs United States University of Bordeaux France Cogitat Ltd. United Kingdom Intelligent Behaviour Understanding Group Imperial College London United Kingdom Aristotle University of Thessaloniki Greece National and Kapodistrian University of Athens Greece DCS Corporation AlexandriaVA United States Human Research and Engineering Directorate DEVCOM Army Research Laboratory Aberdeen Proving GroundMD United States Univ. Grenoble Alpes CEA LETI Clinatec GrenobleF-38000 France Université Paris-Saclay CEA List PalaiseauF-91120 France Warsaw Poland deeptale.ai Poland

Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is because regular machine learning methods cannot generalise well across human subjects and handle learning from different, heterogeneously collected data sets, thus limiting the scale of training data available. On the other hand, the many developments in transfer- and meta-learning fields would benefit significantly from a real-world benchmark with immediate practical application. Therefore, we pick electroencephalography (EEG) as an exemplar for all the things that make biosignal data analysis a hard problem. We design two transfer learning challenges around a. clinical diagnostics and b. neurotechnology. These two challenges are designed to probe algorithmic performance with all the challenges of biosignal data, such as low signal-to-noise ratios, major variability among subjects, differences in the data recording sessions and techniques, and even between the specific BCI tasks recorded in the dataset. Task 1 is centred on the field of medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets. The successful 2021 BEETL competition with its over 30 competing teams and its 3 winning entries brought attention to the potential of deep transfer learning and combinations of set theory and conventional machine learning techniques to overcome the challenges. The results set a new state-of-the-art for the real-world BEETL benchmarks. © 2022 X. Wei et al.

关键词： Electroencephalography

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High-dimensional Gaussian graphical model selection: walk summability and local separation criterion

The Journal of Machine Learning Research

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The Journal of Machine Learning research 2012年第1期13卷

作者： Animashree Anandkumar Vincent Y. F. Tan Furong Huang Alan S. Willsky Electrical Engineering and Computer Science University of California Irvine Irvine CA Data Mining Department Institute for Infocomm Research Singapore Electrical and Computer Engineering National University of Singapore Stochastic Systems Group Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA

We consider the problem of high-dimensional Gaussian graphical model selection. We identify a set of graphs for which an efficient estimation algorithm exists, and this algorithm is based on thresholding of empirical conditional covariances. Under a set of transparent conditions, we establish structural consistency (or sparsistency) for the proposed algorithm, when the number of samples n = Ω(Jmin-2 log p), where p is the number of variables and Jmin is the minimum (absolute) edge potential of the graphical model. The sufficient conditions for sparsistency are based on the notion of walk-summability of the model and the presence of sparse local vertex separators in the underlying graph. We also derive novel non-asymptotic necessary conditions on the number of samples required for sparsistency.

关键词： Gaussian graphical model selection high-dimensional learning local-separation property necessary conditions for model selection walk-summability

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The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel

The Importance of Gender Specification for Detection of Driv...

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Biomedical engineering International Conference (BMEiCON)

作者： Mohammad Shahbakhti Matin Beiramvand Erfan Nasiri Wei Chen Jordi Solé-Casals Michal Wierzchon Anna Broniec-Wójcik Piotr Augustyniak Vaidotas Marozas Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania Faculty of Information Technology and Communication Tampere University Tempere Finland Faculty of Statistics Mathematics and Computer Allameh Tabataba’i University Tehran Iran Center for Intelligent Medical Electronics School of Information Science and Technology the Human Phenome Institute Fudan University Shanghai China Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain Institute of Psychology Jagiellonian University Krakow Poland Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9781665489041

Although detection of the driver fatigue using a single electroencephalography (EEG) channel has been addressed in literature, the gender differentiation for applicability of the model has not been investigated heretofore. Motivated accordingly, we address the detection of driver fatigue based the gender-segregated datasets, where each of them contains 8 subjects. After splitting the EEG signal into its sub-bands (delta, theta, alpha, beta, and gamma) using discrete wavelet transform, the log energy entropy of each band is computed to form the feature vector. Afterwards, the feature vector is randomly split into 50% for training and 50% for the unseen testing, and fed to a support vector machine model. When comparing the classification results of fatigue driving detection between the gender segregated and non-gender segregated datasets, the former achieved the accuracy 78% and 77% for male and female subjects, respectively, than the latter (71%). The obtained results show the importance of gender-specification for the driver fatigue detection.

关键词： Training Support vector machines Computational modeling Transforms Fatigue Brain modeling Electroencephalography

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A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends

arXiv

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arXiv 2023年

作者： Gui, Jie Chen, Tuo Zhang, Jing Cao, Qiong Sun, Zhenan Luo, Hao Tao, Dacheng School of Cyber Science and Engineering Southeast University with Purple Mountain Laboratories Nanjing210000 China School of Cyber Science and Engineering Southeast University China School of Computer Science The University of Sydney CamperdownNSW2050 Australia College of Computing & Data Science Nanyang Technological University #32 Block N4 #02a-014 50 Nanyang Avenue Singapore639798 Singapore JD Explore Academy Center for Research on Intelligent Perception and Computing Chinese Academy of Sciences Beijing100190 China Alibaba Group Hangzhou310052 China

Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be expensive and time-consuming. Self-supervised learning (SSL), a subset of unsupervised learning, aims to learn discriminative features from unlabeled data without relying on human-annotated labels. SSL has garnered significant attention recently, leading to the development of numerous related algorithms. However, there is a dearth of comprehensive studies that elucidate the connections and evolution of different SSL variants. This paper presents a review of diverse SSL methods, encompassing algorithmic aspects, application domains, three key trends, and open research questions. Firstly, we provide a detailed introduction to the motivations behind most SSL algorithms and compare their commonalities and differences. Secondly, we explore representative applications of SSL in domains such as image processing, computer vision, and natural language processing. Lastly, we discuss the three primary trends observed in SSL research and highlight the open questions that remain. A curated collection of valuable resources can be accessed at https://***/guijiejie/SSL. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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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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Understanding the predictability of path flow distribution in urban road networks using an information entropy approach

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Multimodal Transportation 2024年第2期3卷

作者： Guo, Bao Huang, Zhiren Zheng, Zhihao Zhang, Fan Wang, Pu School of Traffic and Transportation Engineering Rail Data Research and Application Key Laboratory of Hunan Province Central South University Changsha China Shandong Provincial Communications Planning and Design Institute Group Co. Ltd Jinan China Department of Computer Science Aalto University Helsinki Finland Department of Civil Engineering McGill University Montreal H3A 0C3 QC Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved. © 2024

关键词： Human mobility Information entropy Path flow distribution Predictability

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Towards Faster Fully Homomorphic Encryption Implementation with Integer and Floating-point Computing Power of GPUs

Towards Faster Fully Homomorphic Encryption Implementation w...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Guang Fan Fangyu Zheng Lipeng Wan Lili Gao Yuan Zhao Jiankuo Dong Yixuan Song Yuewu Wang Jingqiang Lin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing China School of Cryptography University of Chinese Academy of Sciences Beijing China Department of Computer and Software Nanjing University of Information Science and Technology Nanjing China Ant Group Hangzhou China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Cyber Security University of Science and Technology of China Hefei China

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without knowledge of the plaintext message and currently has been the focus of both academia and industry. However, the performance issue hinders its large-scale application, highlighting the urgent requirements of high-performance FHE *** noticing the tremendous potential of GPUs in the field of cryptographic acceleration, this paper comprehensively investigates how to convert the available computing resources residing in GPUs into FHE workhorses, and implement a full set of low-level and middle-level FHE primitives based on two arithmetic units (i.e., INT32 and FP64 units) with three types of data precision (i.e., INT32, INT64 and FP64). This paper gives a comprehensive evaluation and comparison based on each road-map. Our implementations of fundamental functions outperform the implementations on the same platform by 1.7× to 16.7×. Taking CKKS FHE schemes as a case study, our implementation of homomorphic multiplication achieves 3.2× speedup over the state-of-the-art GPU-based implementation, even considering the difference of platforms. The detailed evaluation and comparison of this paper would offer a vital reference for the follow-up work to choose appropriate underlying arithmetic units and important primitive optimizations in GPU-based FHE implementations.

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Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

arXiv

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arXiv 2020年

作者： Chatterjee, Soumick Saad, Fatima Sarasaen, Chompunuch Ghosh, Suhita Krug, Valerie Khatun, Rupali Mishra, Rahul Desai, Nirja Radeva, Petia Rose, Georg Stober, Sebastian Speck, Oliver Nürnberger, Andreas Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Italy Institute for Medical Engineering Otto von Guericke University Magdeburg Germany Research Campus STIMULATE Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Artificial Intelligence Lab Otto von Guericke University Magdeburg Germany Department of Mathematics and Computer Science University of Barcelona Barcelona Spain Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Apollo Hospitals Bilaspur India HCG Cancer Centre Vadodara India Computer Vision Centre Cerdanyola Barcelona Spain Centre for Behavioural Brain Sciences Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing infected patients. Medical imaging, such as X-ray and Computed Tomography (CT), combined with the potential of Artificial Intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2 and DenseNet161) and their ensemble, using majority voting have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods - occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT, and using a global technique - neuron activation profiles. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model. Copyright © 2020, The Authors. All rights reserved.

关键词： COVID-19

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