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arXiv 2019年

作者： Zeng, Jinshan Lin, Shao-Bo Yao, Yuan Zhou, Ding-Xuan School of Computer and Information Engineering Jiangxi Normal University Nanchang China Liu Bie Ju Centre for Mathematical Sciences City University of Hong Kong Hong Kong Hong Kong Department of Mathematics Hong Kong University of Science and Technology Hong Kong Hong Kong 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 Hong Kong Hong Kong

In this paper, we develop an alternating direction method of multipliers (ADMM) for deep neural networks training with sigmoid-type activation functions (called sigmoid-ADMM pair), mainly motivated by the gradient-free nature of ADMM in avoiding the saturation of sigmoid-type activations and the advantages of deep neural networks with sigmoid-type activations (called deep sigmoid nets) over their rectified linear unit (ReLU) counterparts (called deep ReLU nets) in terms of approximation. In particular, we prove that the approximation capability of deep sigmoid nets is not worse than that of deep ReLU nets by showing that ReLU activation function can be well approximated by deep sigmoid nets with two hidden layers and finitely many free parameters but not vice-verse. We also establish the global convergence of the proposed ADMM for the nonlinearly constrained formulation of the deep sigmoid nets training from arbitrary initial points to a Karush-Kuhn-Tucker (KKT) point at a rate of order O(1/k). Besides sigmoid activation, such a convergence theorem holds for a general class of smooth activations. Compared with the widely used stochastic gradient descent (SGD) algorithm for the deep ReLU nets training (called ReLU-SGD pair), the proposed sigmoid-ADMM pair is practically stable with respect to the algorithmic hyperparameters including the learning rate, initial schemes and the pro-processing of the input data. Moreover, we find that to approximate and learn simple but important functions the proposed sigmoid-ADMM pair numerically outperforms the ReLU-SGD pair. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical activation

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Survey on deep learning techniques for person re-identification task

arXiv

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arXiv 2018年

作者： Lavi, Bahram Serj, Mehdi Fatan Ullah, Ihsan Institute of Computing University of Campinas Sao Paulo13083-970 Brazil Department of Computer Engineering and Mathematics University Rovira i Virgili Tarragona Spain Data Mining & Machine Learning Group Discipline of IT National University of Ireland Galway Ireland

Intelligent video-surveillance is currently an active research field in computer vision and machine learning techniques. It provides useful tools for surveillance operators and forensic video investigators. Person reidentification (PReID) is one among these tools. It consists of recognizing whether an individual has already been observed over a camera in a network or not. This tool can also be employed in various possible applications such as off-line retrieval of all the video-sequences showing an individual of interest whose image is given a query, and online pedestrian tracking over multiple camera views. To this aim, many techniques have been proposed to increase the performance of PReID. Among the systems, many researchers utilized deep neural networks (DNNs) because of their better performance and fast execution at test time. Our objective is to provide for future researchers the work being done on PReID to date. Therefore, we summarized state-of-the-art DNN models being used for this task. A brief description of each model along with their evaluation on a set of benchmark datasets is given. Finally, a detailed comparison is provided among these models followed by some limitations that can work as guidelines for future research. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Asymptotically unbiased generative neural sampling

arXiv

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arXiv 2019年

作者： Nicoli, Kim A. Nakajima, Shinichi Strodthoff, Nils Samek, Wojciech Müller, Klaus-Robert Kessel, Pan Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Big Data Center Berlin10587 Germany RIKEN Center for AIP Tokyo103-0027 Japan Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Berliner Zentrum für Maschinelles Lernen Berlin10587 Germany Department of Brain and Cognitive Engineering Korea University Anam-dong Seongbuk-gu Seoul136-713 Korea Republic of Max-Planck-Institut für Informatik Saarbrücken Germany

We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling probability. In this framework, we present asymptotically unbiased estimators for generic observables, including those that explicitly depend on the partition function such as free energy or entropy, and derive corresponding variance estimators. We demonstrate their practical applicability by numerical experiments for the 2d Ising model which highlight the superiority over existing methods. Our approach greatly enhances the applicability of generative neural samplers to real-world physical systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Free energy

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Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt-VANs enhanced by importance and MCMC sampling

arXiv

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arXiv 2019年

作者： Nicoli, Kim Kessel, Pan Strodthoff, Nils Samek, Wojciech Müller, Klaus-Robert Nakajima, Shinichi Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Big Data Center Berlin10587 Germany Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Berliner Zentrum für Maschinelles Lernen Berlin10587 Germany Department of Brain and Cognitive Engineering Korea University Anam-dong Seongbuk-gu Seoul136-713 Korea Republic of Max-Planck-Institut für Informatik Saarbrücken Germany RIKEN Center for AIP Tokyo103-0027 Japan

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Detecting Respiratory Effort-Related Arousals in Polysomnographic data Using LSTM Networks

Detecting Respiratory Effort-Related Arousals in Polysomnogr...

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Computers in Cardiology (CinC)

作者： Sven Schellenberger Kilin Shi Melanie Mai Jan P Wiedemann Tobias Steigleder Björn Eskofier Robert Weigel Alexander Kölpin Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus Germany Institute for Electronics Engineering Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany Department of Palliative Care Universitätsklinikum Erlangen Erlangen Germany Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9781728109244

To diagnose sleep disorders, hours of sleep data from lots of different physiological sensors have to be analyzed. To do so, experts have to look through all the data which is time-consuming and error-prone. Automatic detection and classification of sleep related breathing disorders and arousals would significantly simplify this task. This years Physionet/CinC Challenge deals with this topic. This paper examines the use of a Long Short-Term Memory network for automatic arousal detection. On the test set, an AUPRC score of 0.14 was achieved.

关键词： Training Feature extraction Sleep Electroencephalography Physiology Electromyography Electrocardiography

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Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation

arXiv

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arXiv 2024年

作者： LaBella, Dominic Schumacher, Katherine Mix, Michael Leu, Kevin McBurney-Lin, Shan Nedelec, Pierre Villanueva-Meyer, Javier Shapey, Jonathan Vercauteren, Tom Chia, Kazumi Ivory, Marina Barfoot, Theodore Al-Salihi, Omar Leu, Justin Halasz, Lia Velichko, Yury Wang, Chunhao Kirkpatrick, John Floyd, Scott Reitman, Zachary J. Mullikin, Trey Bagci, Ulas Sachdev, Sean Hattangadi-Gluth, Jona A. Seibert, Tyler M. Farid, Nikdokht Puett, Connor Pease, Matthew W. Shiue, Kevin Anwar, Syed Muhammad Faghani, Shahriar Taylor, Peter Haider, Muhammad Ammar Warman, Pranav Albrecht, Jake Jakab, András Moassefi, Mana Chung, Verena Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Coley, Aaron Huang, Christina Ghanta, Siddharth Schneider, Alex Sharp, Conrad Saluja, Rachit Kofler, Florian Lohmann, Philipp Vollmuth, Phillipp Gagnon, Louis Adewole, Maruf Li, Hongwei Bran Kazerooni, Anahita Fathi Tahon, Nourel Hoda Anazodo, Udunna Moawad, Ahmed W. Menze, Bjoern Linguraru, Marius George Aboian, Mariam Wiestler, Benedikt Baid, Ujjwal Conte, Gian-Marco Rauschecker, Andreas M. Nada, Ayman Abayazeed, Aly H. Huang, Raymond de Verdier, Maria Correia Rudie, Jeffrey D. Bakas, Spyridon Calabrese, Evan Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiation Oncology SUNY Upstate Medical University SyracuseNY United States San FranciscoCA United States Department of Neurosurgery King’s College Hospital London United Kingdom School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Guy’s and St Thomas’ NHS Foundation Trust United Kingdom Department of Radiation Oncology University of Washington SeattleWA United States Department of Radiology Northwestern University EvanstonIL United States Department of Radiation Oncology Northwestern University EvanstonIL United States Department of Radiation Medicine and Applied Sciences University of California San Diego La Jolla CA United States Department of Radiology University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Radiation Oncology Indiana University IndianapolisIN United States Children’s National Hospital WashingtonDC United States George Washington University WashingtonDC United States Mayo Clinic RochesterMN United States CMH Lahore Medical College Lahore Pakistan Duke University Medical Center School of Medicine DurhamNC United States Sage Bionetworks United States University of Zürich Zürich Switzerland Artificial Intelligence Lab Department of Radiology Mayo Clinic RochesterMN United States MLCommons United States Factored AI United States Center For Federated Learning in Medicine Indiana University IndianapolisIN United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Medical Working Group MLCommons San FranciscoCA United States Intel United States Duke Universi

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of 750 radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk postoperative site. Target volume annotations adhere to established radiotherapy planning protocols, ensuring consistency across cases and institutions. For preoperative meningiomas, the target volume encompasses the entire GTV and associated nodular dural tail, while for postoperative cases, it includes at-risk resection cavity margins as determined by the treating institution. Case annotations were reviewed and approved by expert neuroradiologists and radiation oncologists. Participating teams will develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using an adapted lesion-wise Dice Similarity Coefficient and the 95% Hausdorff distance. The top-performing teams will be recognized at the Medical Image Computing and Computer Assisted Intervention Conference in October 2024. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes. © 2024, CC BY.

关键词： Radiotherapy

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A Diagonal-Augmented quasi-Newton method with application to factorization machines

A Diagonal-Augmented quasi-Newton method with application to...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Aryan Mokhtari Amir Ingber Department of Electrical and Systems Engineering University of Pennsylvania USA Big-data Machine Learning Group Yahoo! Sunnyvale CA USA

ISBN: (纸本)9781509041183

We present a novel quasi-Newton method for convex optimization, in which the Hessian estimates are based not only on the gradients, but also on the diagonal part of the true Hessian matrix (which can often be obtained with reasonable complexity). The new algorithm is based on the well known Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm and has similar complexity. The proposed Diagonal-Augmented BFGS (DA-BFGS) method is shown to be stable and achieves a super-linear convergence rate in a local neighborhood of the optimal argument. Numerical experiments on logistic regression and factorization machines problems showcase that DA-BFGS consistently outperforms the baseline BFGS and Newton algorithms.

关键词： Quasi-Newton methods partial Hessian information factorization machines Newton method factorization machine machinery Hessian matrix Logistic Regression Hessian

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Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research

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Digital Biomarkers 2019年第3期3卷 116-132页

作者： Badawy, Reham Hameed, Farhan Bataille, Lauren Little, Max A. Claes, Kasper Saria, Suchi Cedarbaum, Jesse M. Stephenson, Diane Neville, Jon Maetzler, Walter Espay, Alberto J. Bloem, Bastiaan R. Simuni, Tanya Karlin, Daniel R. School of Computer Science University of Birmingham Birmingham United Kingdom Digital Medicine and Pfizer Innovation Research Lab Early Clinical Development Pfizer Inc. CambridgeMA United States College of Computer and Information Science Northeastern University BostonMA United States Analytics Informatics and Business Intelligence Chief Digital Office Pfizer Inc. New YorkNY United States Michael J. Fox Foundation for Parkinson's Research New YorkNY United States Media Lab Massachusetts Institute of Technology CambridgeMA United States UCB Biopharma Brussels Belgium Machine Learning and Healthcare Laboratory Departments of Computer Science Statistics and Health Policy Malone Center for Engineering in Healthcare Armstrong Institute for Patient Safety and Quality Johns Hopkins University BaltimoreMD United States Biogen CambridgeMA United States Critical Path Institute TucsonAZ United States Clinical Data Interchange Standards Consortium AustinTX United States Department of Neurology Christian Albrecht University Kiel Germany James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders University of Cincinnati CincinnatiOH United States Department of Neurology Donders Institute for Brain Cognition and Behavior Radboud University Medical Center Nijmegen Netherlands Department of Neurology Gardner Center for Parkinson's Disease and Movement Disorders UC Gardner Neuroscience Institute University of Cincinnati CincinnatiOH United States Tufts University School of Medicine BostonMA United States HealthMode New YorkNY United States School of Engineering and Applied Science Aston University BirminghamB47ET United Kingdom

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape. © 2019 The Author(s

关键词： Metadata

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Reply to chen et al.: Parametric methods for cluster inference perform worse for two-sided t-tests

arXiv

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arXiv 2018年

作者： Eklund, Anders Knutsson, Hans Nichols, Thomas E. Department of Biomedical Engineering Division of Medical Informatics Linkoping University Linkoping Sweden Department of Computer and Information Science Division of Statistics and Machine Learning Linkoping University Linkoping Sweden Linkoping University Linkoping Sweden Big Data Institute University of Oxford Oxford United Kingdom United Kingdom University of Oxford Oxford United Kingdom Department of Statistics University of Warwick Coventry United Kingdom

One-sided t-tests are commonly used in the neuroimaging field, but two-sided tests should be the default unless a researcher has a strong reason for using a one-sided test. Here we extend our previous work on cluster false positive rates, which used one-sided tests, to two-sided tests. Briey, we found that parametric methods perform worse for two-sided t-tests, and that non-parametric methods perform equally well for one-sided and two-sided tests. Copyright © 2018, The Authors. All rights reserved.

关键词： Neuroimaging

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Cluster failure revisited: Impact of first level design and data quality on cluster false positive rates

arXiv

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arXiv 2018年

作者： Eklund, Anders Knutsson, Hans Nichols, Thomas E. Division of Medical Informatics Department of Biomedical Engineering Linköping University Linköping Sweden Division of Statistics & Machine Learning Department of Computer and Information Science Linköping University Linköping Sweden Linköping University Linköping Sweden Big Data Institute University of Oxford Oxford United Kingdom University of Oxford Oxford United Kingdom Department of Statistics University of Warwick Coventry United Kingdom

Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomisation of events between subjects. We consider the lack of validity found with one-sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all datasets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (P = 0.01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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