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Defending against Membership Inference Attacks on Beacon Services

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arXiv 2021年

作者： Venkatesaramani, Rajagopal Wan, Zhiyu Malin, Bradley A. Vorobeychik, Yevgeniy Washington University in St. Louis Computer Science and Engineering St. Louis63130 United States Vanderbilt University Electrical Engineering and Computer Science Nashville37232 United States Vanderbilt University Medical Center Biomedical Informatics Nashville37203 United States Vanderbilt University Medical Center Biostatistics Nashville37203 United States

Large genomic datasets are now created through numerous activities, including recreational genealogical investigations, biomedical research, and clinical care. At the same time, genomic data has become valuable for reuse beyond their initial point of collection, but privacy concerns often hinder access. Over the past several years, Beacon services have emerged to broaden accessibility to such data. These services enable users to query for the presence of a particular minor allele in a private dataset, information that can help care providers determine if genomic variation is spurious or has some known clinical indication. However, various studies have shown that even this limited access model can leak if individuals are members in the underlying dataset. Several approaches for mitigating this vulnerability have been proposed, but they are limited in that they 1) typically rely on heuristics and 2) offer probabilistic privacy guarantees, but neglect utility. In this paper, we present a novel algorithmic framework to ensure privacy in a Beacon service setting with a minimal number of query response flips (e.g., changing a positive response to a negative). Specifically, we represent this problem as combinatorial optimization in both the batch setting (where queries arrive all at once), as well as the online setting (where queries arrive sequentially). The former setting has been the primary focus in prior literature, whereas real Beacons allow sequential queries, motivating the latter investigation. We present principled algorithms in this framework with both privacy and, in some cases, worst-case utility guarantees. Moreover, through an extensive experimental evaluation, we show that the proposed approaches significantly outperform the state of the art in terms of privacy and utility. © 2021, CC BY.

关键词： Combinatorial optimization

Feasibility of differentially measuring afferent and efferent neural activity with a single nerve cuff electrode

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Feasibility of differentially measuring afferent and efferen...

作者： Sabetian, P. Yoo, P.B. Institute of Biomaterials and Biomedical Engineering University of Toronto Toronto Canada Department of Electrical and Computer Engineering University of Toronto Toronto Canada Toronto Rehabilitation Institute University Health Network University of Toronto Toronto Canada University of Toronto 164 College St TorontoONM5S 3G9 Canada

Objective. Advances in electrode technology have facilitated the development of neuroprostheses for restoring motor/sensory function in disabled individuals. Information extracted from a whole nerve, recorded using cuffs, can provide signals that control the operation of neuroprostheses. However, the amount of information that can be extracted from a tripolar cuff - which provides the highest signal-to-noise ratio (SNR) - is limited. The physical symmetry of the tripolar cuff results in neural recordings that cannot differentiate afferent versus efferent signals. In this study, we introduced a tetrapolar cuff to achieve low-noise and directionally sensitive recording. Approach. The tetrapolar cuff was initially designed using a computational approach. A finite element model was used to solve the electric potential generated at the electrode contacts by active electrical sources, such as the nodes of Ranvier and an artifact noise source. The resulting single fiber action potentials (SFAPs) and artifact noise signals (ANS) were used to characterize the performance of the tetrapolar configuration of the electrode length (EL) and electrode edge length (EEL) on simulated SFAP and ANS. The feasibility of using a tetrapolar cuff to differentiate afferent/efferent action potentials by applying potassium chloride in anesthetized rats was also investigated. Main results. Both the computational and experimental results of this study indicated that directional recording can be achieved using a tetrapolar cuff. Testing different design criteria (e.g. EL and EEL) showed that at EL values above 15 mm and EEL 2 mm, the tetrapolar cuffs can yield larger SNRs than equally-sized tripolar cuffs. Significance. This study indicated that low-noise directionally sensitive measurement of neural activity can be achieved with a tetrapolar cuff. The experimental results confirmed the feasibility of using a tetrapolar cuff to differentiate afferent/efferent signals by applying potassium chlorid

关键词： Signal to noise ratio

Multimodal monitoring of human brain organoids implanted in mice using transparent microelectrodes

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Multimodal monitoring of human brain organoids implanted in ...

Optics and the Brain, BRAIN 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

作者： Thunemann, Martin Wilson, Madison Puppo, Francesca Pizzo, Donald P. Rickner, Hannah Bortz, Emma Mansour, Abed AlFatah Wolozin, Benjamin Han, Xue Gage, Fred H. Muotri, Alysson R. Kuzum, Duygu Devor, Anna Department of Biomedical Engineering Boston University BostonMA02215 United States Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92093 United States Department of Pediatrics University of California San Diego La Jolla CA92093 United States Department of Pathology University of California San Diego La Jolla CA92093 United States Department of Pharmacology Boston University BostonMA02215 United States The Salk Institute for Biological Studies La Jolla CA92093 United States Martinos Center for Biomedical Imaging MGH Harvard Medical School CharlestownMA02129 United States

We present an in vivo platform merging stem cell technology, two-photon imaging, and electrophysiology with transparent graphene microelectrodes for longitudinal interrogation of human brain organoids implanted in the... 详细信息

ISBN: (纸本)9781557528209

关键词： Microelectrodes

Implantable silicon neural probes with nanophotonic phased arrays for single-lobe beam steering

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arXiv 2024年

作者： Chen, Fu-Der Sharma, Ankita Xue, Tianyuan Jung, Youngho Govdeli, Alperen Mak, Jason C.C. Chameh, Homeira Moradi Movahed, Mandana Brunk, Michael G.K. Luo, Xianshu Chua, Hongyao Lo, Patrick Guo-Qiang Valiante, Taufik A. Sacher, Wesley D. Poon, Joyce K.S. Max Planck Institute of Microstructure Physics Weinberg 2 Halle06120 Germany Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S 3G4 Canada Max Planck-University of Toronto Centre for Neural Science and Technology Canada Krembil Brain Institute University Health Network TorontoON Canada Advanced Micro Foundry Pte. Ltd. 11 Science Park Road Singapore Science Park II 117685 Singapore Division of Neurosurgery Department of Surgery Toronto Western Hospital University of Toronto TorontoON Canada Institute of Biomedical Engineering University of Toronto TorontoON Canada

In brain activity mapping experiments using optogenetics, patterned illumination is crucial for deterministic and localized stimulation of neurons. However, due to optical scattering in brain tissue, light-emitting implantable devices are needed to bring precise patterned illumination to deep brain regions. A promising solution is silicon neural probes with integrated nanophotonic circuits that form tailored beam emission patterns without lenses. Here, we demonstrate neural probes with grating-based light emitters that generate a single steerable light beam across > 60% of the steering range with ≥ 4 dB of background suppression for optogenetic photostimulation. The light emitters, optimized for blue or amber light, combine end-fire optical phased arrays with slab gratings to suppress higher-order sidelobes. In vivo optogenetic photostimulation was demonstrated in Thy1-ChR2-YFP mice. These neural probes, capable of continuous single beam scanning, offer a promising avenue for optogenetic stimulation with cellular-level spatial precision in the deep brain. © 2024, CC BY-NC-ND.

关键词： Nanophotonics

Developing a Novel Citizen-Scientist Smartphone App for Collecting Behavioral and Affective Data from Children Populations 8th

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Developing a Novel Citizen-Scientist Smartphone App for Coll...

8th EAI International Conference on Mobile Communication and Healthcare, MobiHealth 2019

作者： Maramis, Christos Ioakimidis, Ioannis Kilintzis, Vassilis Stefanopoulos, Leandros Lekka, Eirini Papapanagiotou, Vasileios Diou, Christos Delopoulos, Anastasios Kassari, Penio Charmandari, Evangelia Maglaveras, Nikolaos Department of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Department of Biosciences and Nutrition Karolinska Institutet Huddinge Sweden Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Biomedical Research Foundation Academy of Athens Athens Greece Department of Industrial Engineering and Management Sciences Northwestern University Evanston United States

ISBN: (纸本)9783030492885

The paradigm of citizen-science, i.e., scientific research that is conducted in whole or in part by non-professional scientists, has gained popularity lately, e.g., for the purpose of crowdsourced data collection. Smartphones with their abundance and ubiquity are perfectly suited and have been widely used for crowdsourced data collection in real life settings. The ongoing, EC-funded research programme named BigO exploits the citizen-science paradigm to collect behavioral (eating, sleeping and physical activity) and affective (mood) data from children populations by means of a novel smartphone application with the intention of developing a decision support system to assist public health authorities in effective policy making against childhood obesity. This paper presents the development – in the context of BigO – of the myBigO app, one of the first citizen-scientist smartphone applications addressed to children for behavioral and affective data collection. This includes the design, implementation, and deployment of myBigO app in a number of data collection studies as well as its preliminary evaluation with respect to technical robustness and user experience in the context of these studies. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2020.

关键词： Decision support systems

AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT: Correlation With Contrast CT Angiography and CMR

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JACC: Advances

JACC: Advances 2024年第10期3卷 101249-101249页

作者： Han, Donghee Shanbhag, Aakash Miller, Robert JH. Kwok, Nicholas Waechter, Parker Builoff, Valerie Newby, David E. Dey, Damini Berman, Daniel S. Slomka, Piotr Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA United States Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Department of Cardiac Sciences University of Calgary Calgary AB Canada British Heart Foundation Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom

Background: Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR). Objectives: The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR. Methods: We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR. The median interval between coronary CTA and CMR was 22 days (interquartile range: 3-76). We utilized a no new UNet AI model that automatically segmented noncontrast CT structures. AI measurement of LV mass was compared to contrast CTA and CMR. Results: A total of 316 patients (age: 57.1 ± 16.7 years, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r = 0.84, P < 0.001), with no significant differences (136.5 ± 55.3 g vs 139.6 ± 56.9 g, P = 0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to CMR, measured LV mass was higher with AI (136.5 ± 55.3 g vs 127.1 ± 53.1 g, P < 0.001). There was an excellent correlation between AI and CMR (r = 0.85, P < 0.001), with a small bias (−9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and CMR or AI and CMR. Conclusions: The AI-based automated estimation of LV mass from noncontrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and CMR. © 2024 The Authors

关键词： artificial intelligence cardiac magnetic resonance imaging computed tomography myocardial mass

Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees

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arXiv 2022年

作者： Gan, Weijie Ying, Chunwei Eshraghi, Parna Wang, Tongyao Eldeniz, Cihat Hu, Yuyang Liu, Jiaming Chen, Yasheng An, Hongyu Kamilov, Ulugbek S. Department of Computer Science & Engineering Washington University in St. Louis St. Louis United States Department of Biomedical Engineering Washington University in St. Louis St. Louis United States Mallinckrodt Institute of Radiology Washington University in St. Louis St. Louis United States Department of Electrical & System Engineering Washington University in St. Louis St. Louis United States Department of Neurology Washington University in St. Louis St. Louis United States

Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding (DU) for image reconstruction. DEQ models—implicit neural networks with effectively infinite number of layers—were shown to achieve state-of-the-art image reconstruction without the memory complexity associated with DU. While the performance of DEQ has been widely investigated, the existing work has primarily focused on the settings where groundtruth data is available for training. We present self-supervised deep equilibrium model (SelfDEQ) as the first self-supervised reconstruction framework for training model-based implicit networks from undersampled and noisy MRI measurements. Our theoretical results show that SelfDEQ can compensate for unbalanced sampling across multiple acquisitions and match the performance of fully supervised DEQ. Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

Multiple-Inputs Convolutional Neural Network for COVID-19 Classification and Critical Region Screening From Chest X-ray Radiographs: Model Development and Performance Evaluation

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JMIR Bioinformatics and Biotechnology 2022年第1期3卷 e36660页

作者： Li, Zhongqiang Li, Zheng Yao, Luke Chen, Qing Zhang, Jian Li, Xin Feng, Ji-Ming Li, Yanping Xu, Jian Division of Electrical and Computer Engineering College of Engineering Louisiana State University Baton Rouge LA United States Department of Visualization Texas A & M University College Station TX United States Department of Comparative Biomedical Science School of Veterinary Medicine Louisiana State University Baton Rouge LA United States School of Environment and Sustainability University of Saskatchewan Saskatoon SK Canada

Background: The COVID-19 pandemic is becoming one of the largest, unprecedented health crises, and chest X-ray radiography (CXR) plays a vital role in diagnosing COVID-19. However, extracting and finding useful image features from CXRs demand a heavy workload for radiologists. Objective: The aim of this study was to design a novel multiple-inputs (MI) convolutional neural network (CNN) for the classification of COVID-19 and extraction of critical regions from CXRs. We also investigated the effect of the number of inputs on the performance of our new MI-CNN model. Methods: A total of 6205 CXR images (including 3021 COVID-19 CXRs and 3184 normal CXRs) were used to test our MI-CNN models. CXRs could be evenly segmented into different numbers (2, 4, and 16) of individual regions. Each region could individually serve as one of the MI-CNN inputs. The CNN features of these MI-CNN inputs would then be fused for COVID-19 classification. More importantly, the contributions of each CXR region could be evaluated through assessing the number of images that were accurately classified by their corresponding regions in the testing data sets. Results: In both the whole-image and left-and right-lung region of interest (LR-ROI) data sets, MI-CNNs demonstrated good efficiency for COVID-19 classification. In particular, MI-CNNs with more inputs (2-, 4-, and 16-input MI-CNNs) had better efficiency in recognizing COVID-19 CXRs than the 1-input CNN. Compared to the whole-image data sets, the efficiency of LR-ROI data sets showed approximately 4% lower accuracy, sensitivity, specificity, and precision (over 91%). In considering the contributions of each region, one of the possible reasons for this reduced performance was that nonlung regions (eg, region 16) provided false-positive contributions to COVID-19 classification. The MI-CNN with the LR-ROI data set could provide a more accurate evaluation of the contribution of each region and COVID-19 classification. Additionally, the right-lung r

关键词： chest X-ray radiography COVID-19 multiple-inputs convolutional neural network screening critical COVID regions

A Framework for Interpretability in Machine Learning for Medical Imaging

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arXiv 2023年

作者： Wang, Alan Q. Karaman, Batuhan K. Kim, Heejong Rosenthal, Jacob Saluja, Rachit Young, Sean I. Sabuncu, Mert R. School of Electrical and Computer Engineering Cornell University Cornell Tech New YorkNY10044 United States Department of Radiology Weill Cornell Medical School New YorkNY10065 United States Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program New YorkNY10065 United States Martinos Center for Biomedical Imaging Harvard Medical School BostonMA02129 United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA02139 United States

Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis and its intersection with machine learning, we identify five core elements of interpretability: localization, visual recognizability, physical attribution, model transparency, and actionability. From this, we arrive at a framework for interpretability in MLMI, which serves as a step-by-step guide to approaching interpretability in this context. Overall, this paper formalizes interpretability needs in the context of medical imaging, and our applied perspective clarifies concrete MLMI-specific goals and considerations in order to guide method design and improve real-world usage. Our goal is to provide practical and didactic information for model designers and practitioners, inspire developers of models in the medical imaging field to reason more deeply about what interpretability is achieving, and suggest future directions of interpretability research. © 2023, CC0.

关键词： Machine learning