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6th International Conference on Computing and informatics, ICCI 2024

作者： Nasr, Mahmoud Piórkowski, Adam Mah, Pascal Muam Abd El-Samie, Fathi E. Department of Biocybemetics and Biomedical Engineering AGH University of Krakow Cracow Poland Faculty of Engineering and Technology Future University in Egypt Cairo Egypt Department of Technical Informatics and Telecommunications AGH University of Krakow Cracow Poland Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

ISBN: (纸本)9798350373875

This study introduces a unique cancelable face recognition technique that depends on adaptive filters and quaternion mathematics. The whale optimization is also applied to determine the optimal parameter settings for best performance. The primary concept of the suggested model revolves around the concealment of facial characteristics before their utilization in the face recognition process. The primary aim of this procedure is to guarantee the confidentiality of users' personal information throughout the enrollment and verification stages of the biometric system, particularly when the database is compromised and the stored biometric templates are altered. In this context, the color facial image is utilized to construct a quaternion. After that, a mask image is employed in a separate quaternion. The cancelable templates are obtained by using the input and mask images represented in quaternion format and applying an adaptive filter on both of them. This process involves preserving the weights of the adaptive filter. Investigation of the suggested tehnique performance demonstrates that the obtained templates lead to low (EER) and a large Area under the ROC curve. © 2024 IEEE.

关键词： Adaptive filters

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Quantifying Strain Dependence of Multi-Frequency Shearwave Elasticity Imaging

Quantifying Strain Dependence of Multi-Frequency Shearwave E...

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Western New York Image Processing Workshop (WNYIPW)

作者： Siladitya Khan Soumya Goswami Fan Feng Marvin M. Doyley Stephen A. McAleavey Departments of Biomedical & Computer Engineering University of Rochester Rochester NY USA Rochester Center for Biomedical Ultrasound University of Rochester Rochester NY USA Goergen Institute for Data Science University of Rochester Rochester NY USA Departments of Electrical & Computer Engineering University of Rochester Rochester NY USA

Nonlinear shear modulus (NLSM) can potentially differentiate benign and malignant pathologies. Previous studies demonstrated the discriminatory value of mapping quantitative estimates of material non-linearity, yet estimates remain biased due to unaccounted shear-wave dispersion within the tissue. We demonstrate a finite element simulation of an inversion technique that combines traditional quasi-static and dynamic elasticity imaging to generate a multi-frequency estimate of the third-order shear modulus A(x,z,ω) over a broad bandwidth (200-600 Hz). We show that our reconstruction results are superior to those obtained by traditional group speed by a factor of two in terms of contrast enhancement measures. Experimental ultrasound elastography on a custom oil-in-gelatin emulsion confirms the presence of dispersion in the NLSM parameter. Constitutive hyper-viscoelastic model responses of spectral NLSM are simulated based on matched linear shear modulus data of experimental phantoms. Hyper-elastic Ogden with a Voigt loss is shown to describe NLSM behavior at frequencies greater than 400 Hz. Reconstruction accuracy in the presence of simulated tracking and correlation noise demonstrates the robustness of our estimates.

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What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding 41

What Improves the Generalization of Graph Transformers? A Th...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Hongkang Wang, Meng Ma, Tengfei Liu, Sijia Zhang, Zaixi Chen, Pin-Yu Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Biomedical Informatics Stony Brook University Stony BrookNY United States Department of Computer Science and Engineering Michigan State University East LansingMI United States MIT-IBM Watson AI Lab IBM Research MA United States Department of Computer Science and Engineering University of Science and Technology of China Anhui Hefei China IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks. Copyright 2024 by the author(s)

关键词： Stochastic systems

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Multi-VentNet: Multimodal Fusion Framework for Predicting Need for Mechanical Ventilation 22

Multi-VentNet: Multimodal Fusion Framework for Predicting Ne...

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22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Lee, Ye Sel Lu, Xiaolei Miller, Michael Lam, Jonathan Y. Shashikumar, Supreeth Nemati, Shamim University of California San Diego Department of Electrical and Computer Engineering La Jolla United States University of California San Diego Department of Biomedical Informatics La Jolla United States University of California San Diego Health Division of Pulmonary and Critical Care La Jolla United States

ISBN: (纸本)9798331520526

Invasive Mechanical Ventilation (MV) is a common and critical intervention for patients with respiratory failure in intensive care units (ICUs). Early identification of patients who will require MV is crucial to improve patient outcomes. Prior studies using single-modality models face challenges in fully capturing complex interrelationships between clinical data sources. To address these challenges, we propose Multi-VentNet, an integrative multimodal fusion approach that combines Electronic Health Records (EHR) and Chest X-rays (CXR) to predict the need for MV up to 24 hours in advance. Our approach consists of three key components: (1) Single-Modality Encoders for extracting modality-specific features from EHR and CXR data, (2) Cross-Modality Encoder to capture inter- and intra-modality relationships, and (3) Risk Integration Module that generates a final risk score by combining predictions from each modality. Experimental results demonstrate that Multi-VentNet achieved an area under the curve (AUC) of 0.8362, a specificity (SPC) of 79.63%, and a positive predictive value (PPV) of 26.41%, outperforming single-modality models by 0.0478 in AUC, 2.01% in SPC, and 1.51% in PPV. These results highlight the effectiveness of our multimodal framework in combining EHR and CXR data for more accurate and timely predictions of MV needs. © 2025 IEEE.

关键词： Electronic health record

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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Solid-State Diffusion: An Introduction

arXiv

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arXiv 2024年

作者： Rondón, Jairo Gonzalez-Lizardo, Angel Bravo, Ricardo Valera, Juan Biomedical & Chemical Engineering Departments Polytechnic University of Puerto Rico San JuanPR United States Department of Electrical and Computer Engineering and Computer Science Polytechnic University of Puerto Rico San JuanPR United States Biomedical Engineering Department Polytechnic University of Puerto Rico San JuanPR United States

This research explores an introduction to solid-state diffusion, focusing on its importance in materials engineering. It examines vacancy and interstitial diffusion mechanisms, the application of Fick’s laws, and their impact on processes such as phase precipitation and recrystallization in metals and alloys. Additionally, it addresses its relevance in grain growth, diffusion welding, and sintering, which are critical processes to improve the properties of materials with engineering applications in various areas such as biomedical, electrical, and chemistry. © 2024, CC BY-NC-ND.

关键词： Grain growth

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Lugsam: A Novel Framework for Integrating Text Prompts to Segment Anything Model (Sam) for Segmentation Tasks of Icu Chest X-Rays

SSRN

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SSRN 2024年

作者： Ramesh, Dhanush Babu Sridhar, Rishika Iytha Upadhyaya, Pulakesh Kamaleswaran, Rishikesan Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States

Chest radiography is a commonly utilized imaging technique for acquiring Chest X-Ray (CXR) images due to its cost-effectiveness and its role in diagnosing lung-related disorders. Nevertheless, interpreting CXR images can be challenging, and the process of separating the lung field from CXR images can be a valuable tool for assessing and diagnosing lung diseases. While various segmentation methods exist, this study primarily focuses on META’s latest Segment Anything Model (SAM). SAM is an Artificial Intelligence (AI) model designed to segment objects within an image. This research aims to harness SAM’s capabilities for segmenting CXR images. Additionally, we explore the potential of another novel model called Grounding DINO. Grounding DINO is a zero-shot object detection model that utilizes a Swin (Shifted Windows) transformer for extracting image features and BERT (Bidirectional Encoder Representations from Transformers) for extracting textual information. It is primarily employed to detect objects in an image based on a provided text prompt, creating bounding boxes around the objects when certain text and box thresholds are met. These bounding boxes are then used as prompts for SAM to generate segmentation masks. The proposed framework has been assessed on CXRs obtained from patients at Emory Hospital in Atlanta, Georgia, USA and further evaluated using NIH clinical center’s CXR image dataset. © 2024, The Authors. All rights reserved.

关键词： Object detection

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Federated learning: Applications, challenges and future directions

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International Journal of Hybrid Intelligent Systems 2022年第1-2期18卷 19-35页

作者： Bharati, Subrato Mondal, M. Rubaiyat Hossain Podder, Prajoy Prasath, V.B. Surya Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center Cincinnati OH United States Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH United States Department of Biomedical Informatics College of Medicine University of Cincinnati Cincinnati OH United States Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati OH United States

Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federated learning systems, with a focus on healthcare. FL is reviewed in terms of its frameworks, architectures and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. Inspired by the rapid growth of FL research, this paper examines recent developments and provides a comprehensive list of unresolved issues. Several privacy methods including secure multiparty computation, homomorphic encryption, differential privacy and stochastic gradient descent are described in the context of FL. Moreover, a review is provided for different classes of FL such as horizontal and vertical FL and federated transfer learning. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis system and healthcare, which we review in this paper. We also present a comprehensive review of existing FL challenges for example privacy protection, communication cost, systems heterogeneity, unreliable model upload, followed by future research directions. © 2022 - IOS Press. All rights reserved.

关键词： Artificial intelligence computing methodologies federated learning healthcare machine learning privacy protection

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Online Transfer Learning for RSV Case Detection

arXiv

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arXiv 2024年

作者： Sun, Yiming Gao, Yuhe Bao, Runxue Cooper, Gregory F. Espino, Jessi Hochheiser, Harry Michaels, Marian G. Aronis, John M. Song, Chenxi Ye, Ye Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States Department of Biomedical Informatics University of Pittsburgh Pittsburgh United States Department of Pediatrics University of Pittsburgh Pittsburgh United States

Transfer learning has become a pivotal technique in machine learning and has proven to be effective in various real-world applications. However, utilizing this technique for classification tasks with sequential data often faces challenges, primarily attributed to the scarcity of class labels. To address this challenge, we introduce Multi-Source Adaptive Weighting (MSAW), an online multi-source transfer learning method. MSAW integrates a dynamic weighting mechanism into an ensemble framework, enabling automatic adjustment of weights based on the relevance and contribution of each source (representing historical knowledge) and target model (learning from newly acquired data). We demonstrate the effectiveness of MSAW by applying it to detect Respiratory Syncytial Virus cases within Emergency Department visits, utilizing multiple years of electronic health records from the University of Pittsburgh Medical Center. Our method demonstrates performance improvements over many baselines, including refining pre-trained models with online learning as well as three static weighting approaches, showing MSAW’s capacity to integrate historical knowledge with progressively accumulated new data. This study indicates the potential of online transfer learning in healthcare, particularly for developing machine learning models that dynamically adapt to evolving situations where new data is incrementally accumulated. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamics

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Controlling gene-expression variability via sequestration-based feedbacks

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IFAC-PapersOnLine 2024年第23期58卷 13-18页

作者： Supravat Dey Cesar A. Vargas-Garcia Abhyudai Singh Department of Physics and Department of Computer Science and Engineering SRM University-AP Amaravati Andhra Pradesh Department of Physics SRM University - AP Amaravati Andhra Pradesh 522240 India Corporación Colombiana de Investigación Agropecuaria - AGROSAVIA Mosquera Colombia Departments of Electrical and Computer Engineering Biomedical Engineering at the University of Delaware Newark DE 19716 USA

Expressed Transcription Factors (TFs) not only bind to sites at target promoters but also to decoy sites scattered across the genome. Binding to such “decoys” sequesters TFs critically impacting the response time and stochasticity (noise) in TF and target gene expression level. When the TF is a stable molecule, whose concentration is diluted by cellular growth, our results show that for fixed mean concentration levels, such decoy bindings can both enhance or suppress random fluctuations in TF levels depending on the source of noise (i.e., intrinsic vs. extrinsic noise) and the strength of binding (i.e., weak vs. strong decoys). We implement negative autoregulation where free (unbound) TF molecules inhibit their synthesis. Our analytical results corroborated by numerical simulations reveal that sequestration accentuates the effects of feedback in the sense that noise attenuation by negative feedback is higher with sequestration than in the absence of feedback. We next consider an alternative form of feedback where the TF increases the production of its decoys, and such feedback architectures are frequently seen in endogenous gene regulation involving microRNA-TF circuits and in controlling cellular stress responses. For these circuits where decoy numbers are TF-regulated, we identify limits of noise suppression, and in many cases, these limits occur at intermediate TF-decoy binding affinities.

关键词： Stochastic gene expression decoys feedback regulation transcription factors

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