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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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Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval

arXiv

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arXiv 2022年

作者： Dong, Jianfeng Wang, Yabing Chen, Xianke Qu, Xiaoye Li, Xirong He, Yuan Wang, Xun The College of Computer and Information Engineering Zhejiang Gongshang University Hangzhou310035 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Electronic Information and Communications Huazhong University of Science and Technology Hubei430074 China The Key Lab of Data Engineering and Knowledge Engineering Renmin University of China The AIMC Lab. School of Information Renmin University of China Beijing100872 China The Alibaba Group Beijing100102 China

This paper aims for the task of text-to-video retrieval, where given a query in the form of a natural-language sentence, it is asked to retrieve videos which are semantically relevant to the given query, from a great number of unlabeled videos. The success of this task depends on cross-modal representation learning that projects both videos and sentences into common spaces for semantic similarity computation. In this work, we concentrate on video representation learning, an essential component for text-to-video retrieval. Inspired by the reading strategy of humans, we propose a Reading-strategy Inspired Visual Representation Learning (RIVRL) to represent videos, which consists of two branches: a previewing branch and an intensive-reading branch. The previewing branch is designed to briefly capture the overview information of videos, while the intensive-reading branch is designed to obtain more in-depth information. Moreover, the intensive-reading branch is aware of the video overview captured by the previewing branch. Such holistic information is found to be useful for the intensive-reading branch to extract more fine-grained features. Extensive experiments on three datasets are conducted, where our model RIVRL achieves a new state-of-the-art on TGIF and VATEX. Moreover, on MSR-VTT, our model using two video features shows comparable performance to the state-of-the-art using seven video features and even outperforms models pre-trained on the large-scale HowTo100M dataset. Code is available at https://***/LiJiaBei-7/rivrl. © 2022, CC BY.

关键词： Video recording

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Newsalyze: Enabling News Consumers to Understand Media Bias

arXiv

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arXiv 2021年

作者： Hamborg, Felix Zhukova, Anastasia Donnay, Karsten Gipp, Bela Dept. of Computer and Information Science University of Konstanz Germany Data & Knowledge Engineering Group University of Wuppertal Wuppertal Germany Dept. of Political Science University of Zurich Switzerland Dept. of Politics and PublicAdministration University of Konstanz Germany

News is a central source of information for individuals to inform themselves on current topics. Knowing a news article’s slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, named bias by word choice and labeling (WCL). WCL bias can alter the assessment of entities reported in the news, e.g., "freedom fighters" vs. "terrorists." At the core of the analysis is a neural model that uses a news-adapted BERT language model to determine target-dependent sentiment, a high-level effect of WCL bias. While the analysis currently focuses on only this form of bias, the visualizations already reveal patterns of bias when contrasting articles (overview) and in-text instances of bias (article view). © 2021, CC BY-SA.

关键词： High level languages

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On the lattice of conceptual measurements

arXiv

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arXiv 2020年

作者： Hanika, Tom Hirth, Johannes Knowledge & Data Engineering Group University of Kassel Germany Interdisciplinary Research Center for Information System Design U Kassel Germany

We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, and derive a canonical representation. Moreover, we prove said scale-measures are lattice ordered with respect to the closure systems. This enables exploring the set of scale-measures through by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of *** Codes 03G10 68T27 Copyright © 2020, The Authors. All rights reserved.

关键词： Formal logic

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MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent

arXiv

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arXiv 2022年

作者： Chatterjee, Soumick Bajaj, Himanshi Siddiquee, Istiyak H. Subbarayappa, Nandish Bandi Simon, Steve Shashidhar, Suraj Bangalore 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 Genomics Research Centre Human Technopole Milan Italy German Centre for Neurodegenerative Disease Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany

Image registration is the process of bringing different images into a common coordinate system - a technique widely used in various applications of computer vision, such as remote sensing, image retrieval, and, most commonly, medical imaging. Deep learning based techniques have been applied successfully to tackle various complex medical image processing problems, including medical image registration. Over the years, several image registration techniques have been proposed using deep learning. Deformable image registration techniques such as Voxelmorph have been successful in capturing finer changes and providing smoother deformations. However, Voxelmorph, as well as ICNet and FIRE, do not explicitly encode global dependencies (i.e. the overall anatomical view of the supplied image) and, therefore, can not track large deformations. In order to tackle the aforementioned problems, this paper extends the Voxelmorph approach in three different ways. To improve the performance in case of small as well as large deformations, supervision of the model at different resolutions has been integrated using a multi-scale UNet. To support the network to learn and encode the minute structural co-relations of the given image-pairs, a self-constructing graph network (SCGNet) has been used as the latent of the multi-scale UNet - which can improve the learning process of the model and help the model to generalise better. And finally, to make the deformations inverse-consistent, cycle consistency loss has been employed. On the task of registration of brain MRIs, the proposed method achieved significant improvements over ANTs and VoxelMorph, obtaining a Dice score of 0.8013±0.0243 for intramodal and 0.6211±0.0309 for intermodal, while VoxelMorph achieved 0.7747±0.0260 and 0.6071±0.0510, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Medical imaging

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Micdir: Multi-Scale Inverse-Consistent Deformable Image Registration Using Unetmss with Self-Constructing Graph Latent

SSRN

引用

SSRN 2022年

作者： Chatterjee, Soumick Bajaj, Himanshi Siddiquee, Istiyak H. Subbarayappa, Nandish Bandi Simon, Steve Shashidhar, Suraj Bangalore 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 Department of Biomedical Magnetic Resonance 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

Image registration is the process of bringing different images into a common coordinate system - a technique widely used in various applications of computer vision, such as remote sensing, image retrieval, and most commonly in medical imaging. Deep Learning based techniques have been applied successfully to tackle various complex medical image processing problems, including medical image registration. Over the years, several image registration techniques have been proposed using deep learning. Deformable image registration techniques such as Voxelmorph have been successful in capturing finer changes and providing smoother deformations. However, Voxelmorph, as well as ICNet and FIRE, do not explicitly encode global dependencies (i.e. the overall anatomical view of the supplied image) and therefore can not track large deformations. In order to tackle the aforementioned problems, this paper extends the Voxelmorph approach in three different ways. To improve the performance in case of small as well as large deformations, supervision of the model at different resolutions have been integrated using a multi-scale UNet. To support the network to learn and encode the minute structural co-relations of the given image-pairs, a self-constructing graph network (SCGNet) has been used as the latent of the multi-scale UNet - which can improve the learning process of the model and help the model to generalise better. And finally, to make the deformations inverse-consistent, cycle consistency loss has been employed. On the task of registration of brain MRIs, the proposed method achieved significant improvements over ANTs and VoxelMorph, obtaining a Dice score of 0.8013±0.0243 for intramodal and 0.6211±0.0309 for intermodal, while VoxelMorph achieved 0.7747±0.0260 and0.6071±0.0510, respectively. © 2022, The Authors. All rights reserved.

关键词： Image registration

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

arXiv

引用

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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Learning structured declarative rule sets — a challenge for deep discrete learning

arXiv

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arXiv 2020年

作者： Fürnkranz, Johannes Hüllermeier, Eyke Mencía, Eneldo Loza Rapp, Michael Computational Data Analytics JKU Linz Austria Heinz Nixdorf Institute Paderborn University Germany Knowledge Engineering Group TU Darmstadt Germany

Arguably the key reason for the success of deep neural networks is their ability to autonomously form non-linear combinations of the input features, which can be used in subsequent layers of the network. The analogon to this capability in inductive rule learning is to learn a structured rule base, where the inputs are combined to learn new auxiliary concepts, which can then be used as inputs by subsequent rules. Yet, research on rule learning algorithms that have such capabilities is still in their infancy, which is — we would argue — one of the key impediments to substantial progress in this field. In this position paper, we want to draw attention to this unsolved problem, with a particular focus on previous work in predicate invention and multi-label rule learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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On Aggregation in Ensembles of Multilabel Classifiers

arXiv

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arXiv 2020年

作者： Nguyen, Vu-Linh Hüllermeier, Eyke Rapp, Michael Mencía, Eneldo Loza Fürnkranz, Johannes Heinz Nixdorf Institute Department of Computer Science Paderborn University Germany Knowledge Engineering Group TU Darmstadt Germany Computational Data Analytics Group JKU Linz Austria

While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far. In this paper, we introduce a formal framework of ensemble multilabel classification, in which we distinguish two principal approaches: "predict then combine" (PTC), where the ensemble members first make loss minimizing predictions which are subsequently combined, and "combine then predict" (CTP), which first aggregates information such as marginal label probabilities from the individual ensemble members, and then derives a prediction from this aggregation. While both approaches generalize voting techniques commonly used for multilabel ensembles, they allow to explicitly take the target performance measure into account. Therefore, concrete instantiations of CTP and PTC can be tailored to concrete loss functions. Experimentally, we show that standard voting techniques are indeed outperformed by suitable instantiations of CTP and PTC, and provide some evidence that CTP performs well for decomposable loss functions, whereas PTC is the better choice for non-decomposable losses. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Computationally Efficient Approximations for Matrix-based Rényi's Entropy

arXiv

引用

arXiv 2021年

作者： Gong, Tieliang Dong, Yuxin Yu, Shujian Dong, Bo The School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an710049 China The Machine Learning Group UiT - The Arctic University of Norway Department of Computer Science Vrije University Amsterdam Amsterdam Netherlands

The recently developed matrix-based Rényi's αorder entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi-definite (PSD) matrices in reproducing kernel Hilbert space, without estimation of the underlying data distribution. This intriguing property makes this new information measurement widely adopted in multiple statistical inference and learning tasks. However, the computation of such quantity involves the trace operator on a PSD matrix G to power α (i.e., tr(Gα)), with a normal complexity of nearly O(n3), which severely hampers its practical usage when the number of samples (i.e., n) is large. In this work, we present computationally efficient approximations to this new entropy functional that can reduce its complexity to even significantly less than O(n2). To this end, we leverage the recent progress on Randomized Numerical Linear Algebra, developing Taylor, Chebyshev and Lanczos approximations to tr(Gα) for arbitrary values of α by converting it into a matrix-vector multiplication problem. We also establish the connection between the matrix-based Rényi's entropy and PSD matrix approximation, which enables exploiting both clustering and block low-rank structure of G to further reduce the computational cost. We theoretically provide approximation accuracy guarantees and illustrate the properties for different approximations. Large-scale experimental evaluations on both synthetic and real-world data corroborate our theoretical findings, showing promising speedup with negligible loss in accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Matrix algebra

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