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AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT

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arXiv 2023年

作者： Lee, Edward H. Kelly, Brendan Altinmakas, Emre Dogan, Hakan Mohammadzadeh, Maryam Colak, Errol Fu, Steve Choudhury, Olivia Ratan, Ujjwal Kitamura, Felipe Chaves, Hernan Zheng, Jimmy Said, Mourad Reis, Eduardo Lim, Jaekwang Yokoo, Patrícia mitchell, Courtney Houshmand, Golnaz Ghassemi, Marzyeh Killeen, Ronan Qiu, Wendy Hayden, Joel Rafiee, Farnaz Klochko, Chad Bevins, Nicholas Sazgara, Faeze Wong, S. Simon Moseley, Michael Halabi, Safwan Yeom, Kristen W. School of Medicine Stanford University United States Insight Centre for Data Analytics University College Dublin Ireland Icahn School of Medicine at Mount Sinai United States Koç University School of Medicine Turkey Division of Neuroradiology Amiralam Hospital Tehran University of Medical Sciences Iran Unity Health Toronto St. Michael’s Hospital University of Toronto Canada Amazon Web Services United States Universidade Federal de São Paulo Brazil Fleni Argentina Radiology Department Centre International Carthage Médicale Monastir Tunisia Hospital Israelita Albert Einstein SP São Paulo Brazil Department of Radiology Kyungpook National University Hospital Korea Republic of Department of Radiology St. Joseph’s Hospital and Medical Center United States Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Iran Dept. of Electrical Engineering and Computer Science at MIT United States Department of Radiology Henry Ford Health System DetroitMI United States Department of Radiology Guilan University of Medical Sciences Iran Department of Electrical Engineering Stanford University United States Ann & Robert H. Lurie Children's Hospital of Chicago United States

While it is well known that population differences from genetics, sex, race, and environmental factors contribute to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We also propose an FL strategy that leverages synthetically generated data to overcome class and size imbalances. We also describe the sources of data heterogeneity in the context of FL, and show how even among the correctly labeled populations, disparities can arise due to these biases. Copyright © 2023, The Authors. All rights reserved.

关键词： Hospitals

Passive Inverted Ultra-Short Baseline (piUSBL) Localization: An Experimental Evaluation of Accuracy

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Passive Inverted Ultra-Short Baseline (piUSBL) Localization:...

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Nicholas R. Rypkema Henrik Schmidt Mechanical Engineering Department Massachusetts Institute of Technology (MIT) 77 Massachusetts Ave Cambridge MA USA Electrical Engineering and Computer Science Department Massachusetts Institute of Technology (MIT) 77 Massachusetts Ave Cambridge MA USA

The underwater environment poses significant challenges for accurate autonomous underwater vehicle (AUV) navigation. Electromagnetic (EM) waves rapidly attenuate due to absorption by water, thereby preventing the use of traditional EM -based positioning methods such as Global Positioning System (GPS) or visible -light cameras. Consequently, underwater positioning is often performed using systems that operate in the hydro -acoustic frequency range (

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Network inference from consensus dynamics with unknown parameters

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arXiv 2019年

作者： Zhu, Yu Schaub, Michael T. Jadbabaie, Ali Segarra, Santiago Department of Electrical and Computer Engineering Rice University United States Institute for Data Systems and Society MIT United States Department of Engineering Science University of Oxford United Kingdom

We explore the problem of inferring the graph Laplacian of a weighted, undirected network from snapshots of a single or multiple discrete-time consensus dynamics, subject to parameter uncertainty, taking place on the network. Specifically, we consider three problems in which we assume different levels of knowledge about the diffusion rates, observation times, and the input signal power of the dynamics. To solve these underdetermined problems, we propose a set of algorithms that leverage the spectral properties of the observed data and tools from convex optimization. Furthermore, we provide theoretical performance guarantees associated with these algorithms. We complement our theoretical work with numerical experiments, that demonstrate how our proposed methods outperform current state-of-the-art algorithms and showcase their effectiveness in recovering both synthetic and real-world networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Dynamics

Decision-making with auto-encoding variational bayes

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arXiv 2020年

作者： Lopez, Romain Boyeau, Pierre Yosef, Nir Jordan, Michael I. Regier, Jeffrey Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Department of Applied Mathematics and Computer Science Ecole des Ponts ParisTech Ragon Institute of MGH MIT and Harvard Cambridge United States Chan-Zuckerberg Biohub San Francisco United States Department of Statistics University of California Berkeley United States Department of Statistics University of Michigan Ann Arbor United States

To make decisions based on a model fit by Auto-Encoding Variational Bayes (AEVB), practitioners typically use importance sampling to estimate a functional of the posterior distribution. The variational distribution found by AEVB serves as the proposal distribution for importance sampling. However, this proposal distribution may give unreliable (high variance) importance sampling estimates, thus leading to poor decisions. We explore how changing the objective function for learning the variational distribution, while continuing to learn the generative model based on the ELBO, affects the quality of downstream decisions. For a particular model, we characterize the error of importance sampling as a function of posterior variance and show that proposal distributions learned with evidence upper bounds are better. Motivated by these theoretical results, we propose a novel variant of the VAE. In addition to experimenting with MNIST, we present a full-fledged application of the proposed method to single-cell RNA sequencing. In this challenging instance of multiple hypothesis testing, the proposed method surpasses the current state of the art. Copyright © 2020, The Authors. All rights reserved.

关键词： Importance sampling

Ethical Machine Learning in Healthcare

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Biomedical Data science 2021年第1期4卷 123-144页

作者： Irene Y. Chen Emma Pierson Sherri Rose Shalmali Joshi Kadija Ferryman Marzyeh Ghassemi 1Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA email: iychen@mit.edu 2Microsoft Research Cambridge Massachusetts 02143 USA 3Center for Health Policy and Center for Primary Care and Outcomes Research Stanford University Stanford California 94305 USA 4Vector Institute Toronto Ontario M5G 1M1 Canada 5Department of Technology Culture and Society Tandon School of Engineering New York University Brooklyn New York 11201 USA 6Institute for Medical and Evaluative Sciences Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.

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Characterization of the John A. Galt telescope for radio holography with CHIME

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arXiv 2022年

作者： Reda, Alex Pinsonneault-Marotte, Tristan Deng, Meiling Amiri, Mandana Bandura, Kevin Chakraborty, Arnab Foreman, Simon Halpern, Mark Hill, Alex S. Höfer, Carolin Kania, Joseph Landecker, T.L. MacEachern, Joshua Masui, Kiyoshi Mena-Parra, Juan Milutinovic, Nikola Newburgh, Laura Ordog, Anna Paul, Sourabh Shaw, J. Richard Siegel, Seth R. Smegal, Rick Wang, Haochen Wulf, Dallas Department of Physics Yale University New HavenCT United States Department of Physics and Astronomy University of British Columbia VancouverBC Canada Dominion Radio Astrophysical Observatory Herzberg Astronomy & Astrophysics Research Centre National Research Council Canada PentictonBC Canada Perimeter Institute for Theoretical Physics WaterlooON Canada Department of Computer Science and Electrical Engineering West Virginia University MorgantownWV United States Center for Gravitational Waves and Cosmology West Virginia University MorgantownWV United States Department of Physics McGill University MontrealQC Canada Department of Computer Science Math Physics and Statistics University of British Columbia-Okanagan KelownaBC Canada Kapteyn Astronomical Institute University of Groningen Groningen Netherlands Department of Physics and Astronomy West Virginia University MorgantownWV United States MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA United States Department of Physics Massachusetts Institute of Technology CambridgeMA United States

The Canadian Hydrogen Intensity Mapping Experiment (CHIME) will measure the 21 cm emission of astrophysical neutral hydrogen to probe large scale structure at redshifts z = 0.8–2.5. However, detecting the 21 cm signal beneath substantially brighter foregrounds remains a key challenge. Due to the high dynamic range between 21 cm and foreground emission, an exquisite calibration of instrument systematics, notably the telescope beam, is required to successfully filter out the foregrounds. One technique being used to achieve a high fidelity measurement of the CHIME beam is radio holography, wherein signals from each of CHIME’s analog inputs are correlated with the signal from a co-located reference antenna, the 26 m John A. Galt telescope, as the 26 m Galt telescope tracks a bright point source transiting over CHIME. In this work we present an analysis of several of the Galt telescope’s properties. We employ drift-scan measurements of several bright sources, along with background estimates derived from the 408 MHz Haslam map, to estimate the Galt system temperature. To determine the Galt telescope’s beam shape, we perform and analyze a raster scan of the bright radio source Cassiopeia A. Finally, we use early holographic measurements to measure the Galt telescope’s geometry with respect to CHIME for the holographic analysis of the CHIME-Galt interferometric data set. © 2022, CC BY.

关键词： Hydrogen

Implications of Delayed Reopening in Controlling the COVID-19 Surge in Southern and West-Central USA

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健康数据科学（英文）

健康数据科学（英文） 2022年第2期 25-34页

作者： Raj Dandekar Emma Wang George Barbastathis Chris Rackauckas Department of Computational Science and Engineering Massachusetts Institute of TechnologyCambridgeUSA MA 02139 Department of Electrical Engineering and Computer Sciences Massachusetts Institute of TechnologyCambridgeMA 02139USA Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Singapore-MIT Alliance for Research and Technology(SMART)Centre Singapore 138602 Department of Applied Mathematics Massachusetts Institute of TechnologyCambridgeMA 02139USA

In the wake of the rapid surge in the COVID-19-infected cases seen in Southern and West-Central USA in the period of June-July 2020,there is an urgent need to develop robust,data-driven models to quantify the effect which early reopening had on the infected case count *** particular,it is imperative to address the question:How many infected cases could have been prevented,had the worst affected states not reopened early?To address this question,we have developed a novel COVID-19 model by augmenting the classical SIR epidemiological model with a neural network *** model decomposes the contribution of quarantine strength to the infection time series,allowing us to quantify the role of quarantine control and the associated reopening policies in the US states which showed a major surge in *** show that the upsurge in the infected cases seen in these states is strongly corelated with a drop in the quarantine/lockdown strength diagnosed by our ***,our results demonstrate that in the event of a stricter lockdown without early reopening,the number of active infected cases recorded on 14 July could have been reduced by more than 40％in all states considered,with the actual number of infections reduced being more than 100,000 for the states of Florida and *** we continue our fight against COVID-19,our proposed model can be used as a valuable asset to simulate the effect of several reopening strategies on the infected count evolution,for any region under consideration.

关键词： covid-19 southern controlling delayed implications reopening surge west-central

Fetal Pose Estimation in Volumetric MRI using a 3D convolution neural network

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arXiv 2019年

作者： Xu, Junshen Zhang, Molin Turk, Esra Abaci Zhang, Larry Grant, Ellen Ying, Kui Golland, Polina Adalsteinsson, Elfar Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Department of Engineering Physics Tsinghua University Beijing China Fetal-Neonatal Neuroimaging Developmental Science Center Boston Childrens Hospital BostonMA United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Harvard Medical School BostonMA United States Institute for Medical Engineering and Science MIT CambridgeMA United States

The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable and rapid on the scale of conventional imaging times, limits the set of viable acquisition techniques to single-shot imaging with severe compromises in signal-tonoise ratio and diagnostic contrast, and frequently results in unacceptable image quality. Surprisingly little is known about the characteristics of fetal motion during MRI and here we propose and demonstrate methods that exploit a growing repository of MRI observations of the gravid abdomen that are acquired at low spatial resolution but relatively high temporal resolution and over long durations (10-30 minutes). We estimate fetal pose per frame in MRI volumes of the pregnant abdomen via deep learning algorithms that detect key fetal landmarks. Evaluation of the proposed method shows that our framework achieves quantitatively an average error of 4.47 mm and 96.4% accuracy (with error less than 10 mm). Fetal pose estimation in MRI time series yields novel means of quantifying fetal movements in health and disease, and enables the learning of kinematic models that may enhance prospective mitigation of fetal motion artifacts during MRI acquisition. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

THE INVERSE EIGENVALUE PROBLEM FOR LINEAR TREES

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arXiv 2019年

作者： Johnson, Charles R. Wakhare, Tanay Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States Department of Mathematics College of William and Mary WilliamsburgVA23185 United States

We prove the sufficiency of the Linear Superposition Principle for linear trees, which characterizes the spectra achievable by a real symmetric matrix whose underlying graph is a linear tree. The necessity was previously proven in [10]. This is the most general class of trees for which the inverse eigenvalue problem has been solved. We explore many consequences, including the Degree Conjecture for possible spectra, upper bounds for the minimum number of eigenvalues of multiplicity 1, and the equality of the diameter of a linear tree and its minimum number of distinct *** Codes 05C50, 15B57 Copyright © 2019, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions