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arXiv 2024年

作者： Christodoulou, Evangelia Reinke, Annika Houhou, Rola Kalinowski, Piotr Erkan, Selen Sudre, Carole H. Burgos, Ninon Boutaj, Sofiène Loizillon, Sophie Solal, Maëlys Rieke, Nicola Cheplygina, Veronika Antonelli, Michela Mayer, Leon D. Tizabi, Minu D. Jorge Cardoso, M. Simpson, Amber Jäger, Paul F. Kopp-Schneider, Annette Varoquaux, Gaël Colliot, Olivier Maier-Hein, Lena Heidelberg Div. Intelligent Medical Systems Germany AI Health Innovation Cluster Germany NCT Heidelberg a partnership between DKFZ Heidelberg University Hospital Germany DKFZ Heidelberg Helmholtz Imaging Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany DKFZ Heidelberg Interactive Machine Learning Group Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London United Kingdom Sorbonne Université Institut du Cerveau - Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié-Salpêtrière France NVIDIA Germany Department of Computer Science IT University of Copenhagen Denmark Centre for Medical Image Computing University College London United Kingdom School of Computing Queen’s University Canada Department of Biomedical and Molecular Sciences Queen’s University Canada Division of Biostatistics DKFZ Germany Parietal project team INRIA Saclay-Île de France France Faculty of Mathematics and Computer Science Heidelberg University Germany Medical Faculty Heidelberg University Germany

Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. In this paper, we argue that this common practice is often a misleading simplification as it ignores performance variability. Our contribution is threefold. (1) Analyzing all MICCAI segmentation papers (n = 221) published in 2023, we first observe that more than 50% of papers do not assess performance variability at all. Moreover, only one (0.5%) paper reported confidence intervals (CIs) for model performance. (2) To address the reporting bottleneck, we show that the unreported standard deviation (SD) in segmentation papers can be approximated by a second-order polynomial function of the mean Dice similarity coefficient (DSC). Based on external validation data from 56 previous MICCAI challenges, we demonstrate that this approximation can accurately reconstruct the CI of a method using information provided in publications. (3) Finally, we reconstructed 95% CIs around the mean DSC of MICCAI 2023 segmentation papers. The median CI width was 0.03 which is three times larger than the median performance gap between the first and second ranked method. For more than 60% of papers, the mean performance of the second-ranked method was within the CI of the first-ranked method. We conclude that current publications typically do not provide sufficient evidence to support which models could potentially be translated into clinical practice. © 2024, CC BY.

关键词： Medical imaging

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VAE-GAN Based Zero-shot Outlier Detection 2020

VAE-GAN Based Zero-shot Outlier Detection

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Proceedings of the 2020 4th International Symposium on Computer science and Intelligent Control

作者： Bekkouch Imad Ibrahim Dragoş Constantin Nicolae Adil Khan Syed Imran Ali Asad Khattak Machine Learning and Knowledge Representation Lab Institute of Data Science & AI Innopolis Tatarstan Russia Institutul de cercetări pentru Inteligenta Artificiala 'Mihai Draganescu' România Department of Computer Engineering Kyung Hee University Yongin-si South Korea College of Technological Innovations Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781450388894

Outlier detection is one of the main fields in machine learning and it has been growing rapidly due to its wide range of applications. In the last few years, deep learning-based methods have outperformed machine learning and handcrafted outlier detection techniques, and our method is no different. We present a new twist to generative models which leverages variational autoencoders as a source for uniform distributions which can be used to separate the inliers from the outliers. Both the generative and adversarial parts of the model are used to obtain three main losses (Reconstruction loss, KL-divergence, Discriminative loss) which in return are wrapped with a one-class SVM which is used to make the predictions. We evaluated our method against several datasets both for images and tabular data and it has shown great results for the zero-shot outlier detection problem and was able to easily generalize it for supervised outlier detection tasks on which the performance has increased. For comparison, we evaluated our method against several of the common outlier detection techniques such as DBSCAN-based outlier detection, GMM, K-means and one class SVM directly, and we have outperformed all of them on all datasets.

关键词： Deep learning Generative Models machine learning Outlier Detection

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Front Cover: Generation of Molecular Counterfactuals for Explainable machine learning Based on Core-Substituent Recombination (ChemMedChem 3/2024)

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ChemMedChem 2024年第3期19卷

作者： Alec Lamens Prof. Jürgen Bajorath Department of Life Science Informatics and Data Science B-IT LIMES Program Unit Chemical Biology and Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 5/6 53115 Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Rheinische Friedrich-Wilhelms-Universität Bonn Friedrich-Hirzebruch-Allee 5/6 53115 Bonn Germany

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State of the art of machine learning: An overview of the past, current, and the future research trends in the era of quantum computing

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AIP Conference Proceedings 2022年第1期2641卷

作者： Mohammad Isa Irawan Mohammad Jamhuri 1)Laboratory of Machine Learning and Big Data Departement Mathematics Faculty of Sciences and Analytical Data Institut Teknologi Sepuluh Nopember Surabaya Indonesia. 2)Department of Mathematics Faculty of Science and Technology UIN Maulana Malik Ibrahim Malang Indonesia

This paper describes data science history and behavioral trends on the largest platform for learning and competition in analyzing and modeling data; Kaggle. We analyze the history of methods commonly used in linear predictor to predict, classify, cluster, and explore data sets. In addition, we also examine the use of the most widely used tools and frameworks to help make data modeling easier. The analysis was carried out on the forum discussion data for the last ten years based on the data available on meta-Kaggle. To see the future trend of data science and linear predictor models, we analyzed the abstracts on the articles available on the Elsevier search page. We extracted information from them using a machine learning method.

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Distributed Kernel Ridge Regression with Communications

arXiv

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arXiv 2020年

作者： Lin, Shao-Bo Wang, Di Zhou, Ding-Xuan Center of Intelligent Decision-Making and Machine Learning School of Management Xi’an Jiaotong University Xi’an China School of Data Science Department of Mathematics City University of Hong Kong Kowloon Hong Kong

This paper focuses on generalization performance analysis for distributed algorithms in the framework of learning theory. Taking distributed kernel ridge regression (DKRR) for example, we succeed in deriving its optimal learning rates in expectation and providing theoretically optimal ranges of the number of local processors. Due to the gap between theory and experiments, we also deduce optimal learning rates for DKRR in probability to essentially reflect the generalization performance and limitations of DKRR. Furthermore, we propose a communication strategy to improve the learning performance of DKRR and demonstrate the power of communications in DKRR via both theoretical assessments and numerical experiments. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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TUdataset: A collection of benchmark datasets for learning with graphs

arXiv

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arXiv 2020年

作者： Morris, Christopher Kriege, Nils M. Bause, Franka Kersting, Kristian Mutzel, Petra Neumann, Marion CERC in Data Science for Real-Time Decision-Making Poly-technique MontrÃl’al Faculty of Computer Science University of Vienna Department of Computer Science TU Dortmund University Machine Learning Group TU Darmstadt Department of Computer Science University of Bonn Department of Computer Science and Engineering Washington University in St. Louis

Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark datasets and standardized evaluation procedures is lagging, consequently hindering advancements in this area. To address this, we introduce the TUdataSET for graph classification and regression. The collection consists of over 120 datasets of varying sizes from a wide range of applications. We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools. Here, we give an overview of the datasets, standardized evaluation procedures, and provide baseline experiments. All datasets are available at ***. The experiments are fully reproducible from the code available at ***/chrsmrrs/tudataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Advances in machine and deep learning for modeling and real-time detection of multi-messenger sources

arXiv

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arXiv 2021年

作者： Huerta, Eliu A. Zhao, Zhizhen Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mathematics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Statistics National Center for Supercomputing Applications Center for Artificial Intelligence Innovation University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the Universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos and cosmic rays cover a wide range of wavelengths and time scales. Combining and processing these datasets that vary in volume, speed and dimensionality requires new modes of instrument coordination, funding and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets, have powered the rise of artificial intelligence. This digital revolution now powers a multi-billion dollar industry, with far-reaching implications in technology and society. In this chapter we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in Multi-Messenger Astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017, to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and *** Codes 83-02, 83-04, 83-08, 85-08, 85-10, 68T07, 68T20, Copyright © 2021, The Authors. All rights reserved.

关键词： Gravity waves

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Vox2Vox: 3D-GAN for Brain Tumour Segmentation

arXiv

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arXiv 2020年

作者： Cirillo, Marco Domenico Abramian, David Eklund, Anders Department of Biomedical Engineering Linköping University Linköping Sweden Center for Medical Image Science and Visualization Linköping University Linköping Sweden Division of Statistics and Machine learning Department of Computer and Information Science Linköping University Linköping Sweden

We propose a 3D volume-to-volume Generative Adversarial Network (GAN) for segmentation of brain tumours. The proposed model, called Vox2Vox, generates segmentations from multi-channel 3D MR images. The best results are obtained when the generator loss (a 3D U-Net) is weighted 5 times higher compared to the discriminator loss (a 3D GAN). For the BraTS 2018 training set we obtain (after ensembling 5 models) the following dice scores and Hausdorff 95 percentile distances: 90.66%, 82.54%, 78.71%, and 4.04 mm, 6.07 mm, 5.00 mm, for whole tumour, core tumour and enhancing tumour respectively. The proposed model is shown to compare favorably to the winners of the BraTS 2018 challenge, but a direct comparison is not possible. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Online Parameter-Free learning of Multiple Low Variance Tasks

arXiv

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arXiv 2020年

作者： Denevi, Giulia Stamos, Dimitris Pontil, Massimiliano Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genova16163 Italy Computer Science Department University College of London LondonWC1E 6BT United Kingdom

We propose a method to learn a common bias vector for a growing sequence of low-variance tasks. Unlike state-of-the-art approaches, our method does not require tuning any hyper-parameter. Our approach is presented in the non-statistical setting and can be of two variants. The "aggressive" one updates the bias after each datapoint, the "lazy" one updates the bias only at the end of each task. We derive an across-tasks regret bound for the method. When compared to state-of-the-art approaches, the aggressive variant returns faster rates, the lazy one recovers standard rates, but with no need of tuning hyper-parameters. We then adapt the methods to the statistical setting: the aggressive variant becomes a multi-task learning method, the lazy one a meta-learning method. Experiments confirm the effectiveness of our methods in practice. Copyright © 2020, The Authors. All rights reserved.

关键词： E-learning

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Ambiguity measures for preference-based decision viewpoints 7th

Ambiguity measures for preference-based decision viewpoints

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7th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2019

作者： Franco, Camilo Rodríguez, J. Tinguaro Montero, Javier Gómez, Daniel Yager, Ronald R. Department of Industrial Engineering Andes University Bogotá111711 Colombia Department of Statistics and Operational Research Complutense University Madrid28040 Spain Statistics and Data Science Complutense University Madrid28040 Spain Machine Intelligence Institute Iona College New RochelleNY10801 United States

ISBN: (纸本)9783030148140

This paper examines the ambiguity of subjective judgments, which are represented by a system of pairwise preferences over a given set of alternatives. Such preferences are valued with respect to a set of reasons, in favor and against the alternatives, establishing a complete judgment, or viewpoint, on how to solve the decision problem. Hence, viewpoints entail particular decisions coming from the system of preferences, where the preference-based reasoning of a given viewpoint holds according to its soundness or coherence. Here we explore such a coherence under the frame of ambiguity measures, aiming at learning viewpoints with highest preference-score and minimum ambiguity. We extend existing measures of ambiguity into a multi-dimensional fuzzy setting, and suggest some future lines of research towards measuring the coherence or (ir)rationality of viewpoints, exploring the use of information measures in the context of preference learning. © Springer Nature Switzerland AG 2019.

关键词： Information use

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