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arXiv 2023年

作者： Zhao, Chen Liu, Anqi Zhang, Xiao Cao, Xuewei Ding, Zhengming Sha, Qiuying Shen, Hui Deng, Hong-Wen Zhou, Weihua Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Mathematical Sciences Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Department of Computer Science Tulane University New OrleansLA70118 United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning, which is used to maximize the mutual information between different types of omics, is employed before latent feature concatenation. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicated that the proposed CLCLSA outperformed the state-of-the-art approaches for multi-omics data classification using incomplete multiomics data. © 2023, CC BY.

关键词： Data integration

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An efficient end-to-end computational framework for the generation of ECG calibrated volumetric models of human atrial electrophysiology

arXiv

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arXiv 2025年

作者： Zappon, Elena Azzolin, Luca Gsell, Matthias A.F. Thaler, Franz Prassl, Anton J. Arnold, Robert Gillette, Karli Kariman, Mohammadreza Manninger-Wünscher, Martin Scherr, Daniel Neic, Aurel Urschler, Martin Augustin, Christoph M. Vigmond, Edward J. Plank, Gernot Division of Biophysics Medical University of Graz Graz Austria BioTechMed-Graz Graz Austria NumeriCor Gmbh Graz Austria Institute of Visual Computing Graz University of Technology Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria Scientific Computing and Imaging Institute University of Utah United States Department of Biomedical Engineering University of Utah United States Clinical Department of Cardiology Medical University of Graz Graz Austria University of Bordeaux CNRS Bordeaux France

Computational models of atrial electrophysiology (EP) are increasingly utilized for applications such as the development of advanced mapping systems, personalized clinical therapy planning, and the generation of virtual cohorts and digital twins. These models have the potential to establish robust causal links between simulated in silico behaviors and observed human atrial EP, enabling safer, cost-effective, and comprehensive exploration of atrial dynamics. However, current state-of-the-art approaches lack the fidelity and scalability required for regulatory-grade applications, particularly in creating high-quality virtual cohorts or patient-specific digital twins. Challenges include anatomically accurate model generation, calibration to sparse and uncertain clinical data, and computational efficiency within a streamlined workflow. This study addresses these limitations by introducing novel methodologies integrated into an automated end-to-end workflow for generating high-fidelity digital twin snapshots and virtual cohorts of atrial EP. These innovations include: (i) automated multi-scale generation of volumetric biatrial models with detailed anatomical structures and fiber architecture;(ii) a robust method for defining space-varying atrial parameter fields;(iii) a parametric approach for modeling inter-atrial conduction pathways;and (iv) an efficient forward EP model for high-fidelity electro-cardiogram (ECG) computation. We evaluated this workflow on a cohort of 50 atrial fibrillation patients, producing high-quality meshes suitable for reaction-eikonal and reaction-diffusion models and demonstrating the ability to simulate atrial ECGs under parametrically controlled conditions. These advancements represent a critical step toward scalable, precise, and clinically applicable digital twin models and virtual cohorts, enabling enhanced patient-specific predictions and therapeutic *** Codes 92C50, 92C55, 92C30, 35Q92 Copyright © 2025, The Authors. All rights r

关键词： Virtual addresses

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A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture

arXiv

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arXiv 2024年

作者： Shaik, Anjum Larsen, Kristoffer Lane, Nancy E. Zhao, Chen Su, Kuan-Jui Keyak, Joyce H. Tian, Qing Sha, Qiuying Shen, Hui Deng, Hong-Wen Zhou, Weihua Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Department of Mathematical Sciences Michigan Technological University HoughtonMI United States Department of Internal Medicine Division of Rheumatology UC Davis Health SacramentoCA95817 United States Department of Computer Science Kennesaw State University 680 Arntson Dr MariettaGA30060 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Radiological Sciences Department of Biomedical Engineering Department of Mechanical and Aerospace Engineering University of California IrvineCA United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using convolutional neural networks (CNNs) to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. The study cohort included 547 patients, with 94 experiencing hip fracture. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an AUC of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning. It effectively balanced accuracy and specificity, offering a robust solution when DXA data acquisition is not always feasible. Statistical tests confirmed significant differences between the models, hi

关键词： Machine learning

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WebAssembly enables low latency interoperable augmented and virtual reality software

arXiv

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arXiv 2021年

作者： Kim, Woo Jae Khomtchouk, Bohdan B. The College of the University of Chicago ChicagoIL United States Department of Biomedical Engineering & Informatics Luddy School of Informatics Computing and Engineering Indiana University Indianapolis IndianapolisIN United States

There is a clear difference in runtime performance between native applications that use augmented/virtual reality (AR/VR) device-specific hardware and comparable web-based implementations. Here we show that WebAssembly (Wasm) offers a promising developer solution that can bring near-native low latency performance to web-based applications, enabling hardware-agnostic interoperability at scale through portable bytecode that runs on any WiFi or cellular data network-enabled AR/VR device. Many software application areas have begun to realize Wasm’s potential as a key enabling technology, but it has yet to establish a robust presence in the AR/VR domain. When considering the limitations of current web-based AR/VR development technologies such as WebXR, which provides an existing application programming interface (API) that enables AR/VR capabilities for web-based programs, Wasm can resolve critical issues faced with just-in-time (JIT) compilation, slow run-times, large file sizes and big data, among other challenges. Existing applications using Wasm-based WebXR are sparse but growing, and the potential for porting native applications to use this emerging framework will benefit the web-based AR/VR application space and bring it closer to its native counterparts in terms of performance. Taken together, this kind of standardized "write-once-deploy-everywhere" software framework for AR/VR applications has the potential to consolidate user experiences across different head-mounted displays and other embedded devices to ultimately create an interoperable AR/VR ecosystem (Jacobsson and Willén (2020)). © 2021, CC BY.

关键词： Application programs

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A Staged Approach Using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture

SSRN

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SSRN 2024年

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant *** study cohort included 547 patients, with 94 experiencing hip fracture. To assess the risk of hip fracture, clinical variables and clinical variables combined with hip DXA imaging features were evaluated as predictors, followed by a novel staged approach. Hip DXA imaging features included those extracted by convolutional neural networks (CNNs), shape measurements, and texture features. Two ensemble machine learning models were evaluated: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and imaging features) using the logistic regression as the base classifier and bootstrapping for ensemble learning. The staged approach was developed using uncertainty quantification from Ensemble 1 which was used to decide if hip DXA imaging features were necessary to improve prediction for each subject. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an AUC of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning, effectively balancing accuracy and specificity, while offering a robust solution when DXA data acquisition is not f

关键词： Machine learning

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Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics

arXiv

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arXiv 2023年

作者： Zhao, Chen Su, Kuan-Jui Wu, Chong Cao, Xuewei Sha, Qiuying Li, Wu Luo, Zhe Qin, Tian Qiu, Chuan Zhao, Lan Juan Liu, Anqi Jiang, Lindong Zhang, Xiao Shen, Hui Zhou, Weihua Deng, Hong-Wen Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI4993 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Biostatistics University of Texas MD Anderson Pickens Academic Tower 1400 Pressler St. HoustonTX77030 United States Department of Mathematical Sciences Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States Tulane Center for Biomedical Informatics and Genomics Deming Department of Medicine Tulane University 1440 Canal Street Suite 1619F New OrleansLA70112 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-view variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. Results: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved RR2-scores > 0.01 for 71.55% of metabolites. Conclusion: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research. © 2023, CC BY.

关键词： Metabolites

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Extended Graph Assessment Metrics for Graph Neural Networks

arXiv

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arXiv 2023年

作者： Mueller, Tamara T. Starck, Sophie Feiner, Leonhard F. Bintsi, Kyriaki-Margarita Rueckert, Daniel Kaissis, Georgios Institute for AI in Medicine and Healthcare Faculty of Informatics Technical University of Munich Germany Department of Diagnostic and Interventional Radiology Faculty of Medicine Technical University of Munich Germany BioMedIA Department of Computing Imperial College London United Kingdom Institute for Machine Learning in Biomedical Imaging Helmholtz-Zentrum Munich Germany

When re-structuring patient cohorts into so-called population graphs, initially independent patients can be incorporated into one interconnected graph structure. This population graph can then be used for medical downstream tasks using graph neural networks (GNNs). The construction of a suitable graph structure is a challenging step in the learning pipeline that can have severe impact on model performance. To this end, different graph assessment metrics have been introduced to evaluate graph structures. However, these metrics are limited to classification tasks and discrete adjacency matrices, only covering a small subset of real-world applications. In this work, we introduce extended graph assessment metrics (GAMs) for regression tasks and weighted graphs. We focus on two GAMs in specific: homophily and cross-class neighbourhood similarity (CCNS). We extend the notion of GAMs to more than one hop, define homophily for regression tasks, as well as continuous adjacency matrices, and propose a light-weight CCNS distance for discrete and continuous adjacency matrices. We show the correlation of these metrics with model performance on different medical population graphs and under different learning settings, using the TADPOLE and UKBB datasets. © 2023, CC BY.

关键词： Graph neural networks

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Pan-Cancer Tumor Infiltrating Lymphocyte Detection based on Federated Learning

Pan-Cancer Tumor Infiltrating Lymphocyte Detection based on ...

引用

IEEE International Conference on Big Data

作者： Ujjwal Baid Sarthak Pati Tahsin M. Kurc Rajarsi Gupta Erich Bremer Shahira Abousamra Siddhesh P. Thakur Joel H. Saltz Spyridon Bakas Department of Pathology & Laboratory Medicine Division of Computational Pathology Indiana University School of Medicine Indianapolis IN USA Department of Radiology & Imaging Sciences Indiana University School of Medicine Indianapolis IN USA Indiana University Melvin and Bren Simon Comprehensive Cancer Center Indianapolis IN USA Department of Informatics Technical University of Munich Munich Germany Department of Biomedical Informatics Stony Brook University Stony Brook NY USA Department of Computer Science Stony Brook University Stony Brook NY USA Department of Computer Science Luddy School of Informatics Computing and Engeineering Indiana University Indianapolis IN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Advances in deep learning (DL) have shown great promise in revolutionizing healthcare, notwithstanding their success hinging on the availability of centralized large and diverse data. Such centralization is challenging because of numerous concerns relating to privacy, data-ownership, intellectual property, and compliance with varying regulatory policies. Federated learning (FL), offers a new decentralized paradigm to train DL models in healthcare. In this study, we evaluate the effect of FL in developing DL models for the analysis of digitized tissue sections, specifically whole slide images (WSIs). A classification application was considered as the example use case, to quantify the distribution of Tumor Infiltrating Lymphocytes (TILs), which are a critical biomarker in cancer research, providing valuable insights into patient outcomes. We trained a VGG classification model using 50 × 50 micron patches extracted from the WSIs with their associated TIL/nonTIL label. We simulated a FL environment, where different cancer types are included across each collaborating node. Our results show that the model trained with the federated training approach achieves similar performance, both quantitatively and qualitatively, to that of a model trained with all the training data pooled at a centralized location. Our study shows that FL has tremendous potential for enabling the development of more robust and accurate models for histopathology image analysis without having to collect large and diverse training data at a single location. Particularly for TILs, our FL approach yields a single DL model trained across numerous anatomical sites and able to robustly generalize to unseen cancer types.

关键词： Training Analytical models Federated learning Biological system modeling Lymphocytes Training data Medical services Data models Cancer Tumors

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HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing

arXiv

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arXiv 2022年

作者： Sivarajkumar, Sonish Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PA United States Department of Biomedical Informatics University of Pittsburgh PA United States Department of Health Information Management University of Pittsburgh PA United States

Deep learning algorithms are dependent on the availability of large-scale annotated clinical text datasets. The lack of such publicly available datasets is the biggest bottleneck for the development of clinical Natural Language Processing(NLP) systems. Zero-Shot Learning(ZSL) refers to the use of deep learning models to classify instances from new classes of which no training data have been seen before. Prompt-based learning is an emerging ZSL technique where we define task-based templates for NLP tasks. We developed a novel prompt-based clinical NLP framework called HealthPrompt and applied the paradigm of prompt-based learning on clinical texts. In this technique, rather than fine-tuning a Pre-trained Language Model(PLM), the task definitions are tuned by defining a prompt template. We performed an in-depth analysis of HealthPrompt on six different PLMs in a no-data setting. Our experiments prove that prompts effectively capture the context of clinical texts and perform remarkably well without any training data. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

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The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning

arXiv

引用

arXiv 2023年

作者： Prokopenko, Denis Hammernik, Kerstin Roberts, Thomas Lloyd, David F.A. Rueckert, Daniel Hajnal, Joseph V. Biomedical Engineering Department School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Department of Informatics Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Clinical Scientific Computing Guy’s & St. Thomas’ NHS Foundation Trust London United Kingdom Child Health King’s College London London United Kingdom Paediatric and Fetal Cardiology Evelina London Children’s Hospital London United Kingdom

Dynamic free-breathing fetal cardiac MRI is one of the most challenging modalities, which requires high temporal and spatial resolution to depict rapid changes in a small fetal heart. The ability of deep learning methods to recover undersampled data could help to optimise the kt-SENSE acquisition strategy and improve non-gated kt-SENSE reconstruction quality. In this work, we explore supervised deep learning networks for reconstruction of kt-SENSE style acquired data using an extensive in vivo dataset. Having access to fully-sampled low-resolution multi-coil fetal cardiac MRI, we study the performance of the networks to recover fully-sampled data from undersampled data. We consider model architectures together with training strategies taking into account their application in the real clinical setup used to collect the dataset to enable networks to recover prospectively undersampled data. We explore a set of modifications to form a baseline performance evaluation for dynamic fetal cardiac MRI on real data. We systematically evaluate the models on coil-combined data to reveal the effect of the suggested changes to the architecture in the context of fetal heart properties. We show that the best-performers recover a detailed depiction of the maternal anatomy on a large scale, but the dynamic properties of the fetal heart are under-represented. Training directly on multi-coil data improves the performance of the models, allows their prospective application to undersampled data and makes them outperform CTFNet introduced for adult cardiac cine MRI. However, these models deliver similar qualitative performances recovering the maternal body very well but underestimating the dynamic properties of fetal heart. This dynamic feature of fast change of fetal heart that is highly localised suggests both more targeted training and evaluation methods might be needed for fetal heart application. © 2023, CC BY.

关键词： Image reconstruction

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