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Advancing Neonatal Care: A Deep learning Approach for Non-Contact Heart Rate Monitoring

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Advancing Neonatal Care: A Deep Learning Approach for Non-Co...

2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Grafton, Alex Castelblanco, Alejandra Warnecke, Joana M. Thomson, Lynn Schubert, Benjamin Hilgendorff, Anne Schnabel, Julia A. Lasenby, Joan Beardsall, Kathryn Signal Processing and Communications Laboratory Engineering Department Cambridge University United Kingdom Computational Health Center Helmholtz Munich Germany School of Computation Information and Technology Technical University of Munich Germany University of Cambridge Department of Paediatrics United Kingdom Munich Germany Dr. von Hauner Children’s Hospital Hospital of the Ludwig Maximilian Universität München Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany

ISBN: (纸本)9798350350548

Heart rate is an important indicator of newborn health status. Conventional wired heart rate monitoring is affected by motion, can limit parental bonding and is prone to damage the fragile newborn skin. Video-based heart rate monitoring in adults has shown potential for the assessment of cardiac functions in optimal acquisition conditions. However, automated methods adapted for neonates, trained with limited sample sizes and capable of handling occlusions and variable illumination, still need to be explored. This work proposes a new deep-learning pipeline for video-based neonatal heart rate measurement, integrating color and infrared signals from multiple neonatal body regions. We train deep learning models for pose estimation and heart rate detection in a pilot cohort of five neonates recorded in the clinic for up to 60 minutes. Our methods show generalization in a leave-one-out cross-validation scheme;the newborn pose estimation model presents high performance (average precision=0.85±0.07), while the heart rate detection model achieves a mean absolute error of 3.83±1.22 beats per minute compared to the electrocardiogram heart rate. Automated video-based heart rate measurement could provide a non-contact, low-cost complement to current HR monitoring technologies in the clinic or outpatient settings. © 2024 IEEE.

关键词： Neonatal monitoring

Unsupervised Analysis of Alzheimer’s Disease Signatures using 3D Deformable Autoencoders

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arXiv 2024年

作者： Avci, Mehmet Yigit Chan, Emily Zimmer, Veronika Rueckert, Daniel Wiestler, Benedikt Schnabel, Julia A. Bercea, Cosmin I. Technical University of Munich Germany Helmholtz AI Institute of Machine Learning in Biomedical Imaging Munich Germany Klinikum Rechts der Isar Munich Germany Imperial College London United Kingdom School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom

With the increasing incidence of neurodegenerative diseases such as Alzheimer’s Disease (AD), there is a need for further research that enhances detection and monitoring of the diseases. We present MORPHADE (Morphological Autoencoders for Alzheimer’s Disease Detection), a novel unsupervised learning approach which uses deformations to allow the analysis of 3D T1-weighted brain images. To the best of our knowledge, this is the first use of deformations with deep unsupervised learning to not only detect, but also localize and assess the severity of structural changes in the brain due to AD. We obtain markedly higher anomaly scores in clinically important areas of the brain in subjects with AD compared to healthy controls, showcasing that our method is able to effectively locate AD-related atrophy. We additionally observe a visual correlation between the severity of atrophy highlighted in our anomaly maps and medial temporal lobe atrophy scores evaluated by a clinical expert. Finally, our method achieves an AUROC of 0.80 in detecting AD, out-performing several supervised and unsupervised baselines. We believe our framework shows promise as a tool towards improved understanding, monitoring and detection of AD. To support further research and application, we have made our code publicly available at ***/ci-ber/MORPHADE. © 2024, CC BY.

关键词： Unsupervised learning

Physics-Informed Deep learning for Motion-Corrected Reconstruction of Quantitative Brain MRI

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arXiv 2024年

作者： Eichhorn, Hannah Spieker, Veronika Hammernik, Kerstin Saks, Elisa Weiss, Kilian Preibisch, Christine Schnabel, Julia A. Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany School of Computation Information & Technology TUM Germany School of Medicine & Health TUM Germany Philips GmbH Market DACH Germany School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom

We propose PHIMO, a physics-informed learning-based motion correction method tailored to quantitative MRI. PHIMO leverages information from the signal evolution to exclude motion-corrupted k-space lines from a data-consistent reconstruction. We demonstrate the potential of PHIMO for the application of T2* quantification from gradient echo MRI, which is particularly sensitive to motion due to its sensitivity to magnetic field inhomogeneities. A state-of-the-art technique for motion correction requires redundant acquisition of the k-space center, prolonging the acquisition. We show that PHIMO can detect and exclude intra-scan motion events and, thus, correct for severe motion artifacts. PHIMO approaches the performance of the state-of-the-art motion correction method, while substantially reducing the acquisition time by over 40%, facilitating clinical applicability. Our code is available at https://***/HannahEichhorn/PHIMO. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Mask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge

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arXiv 2024年

作者： Fischer, Stefan M. Kiechle, Johannes Lang, Daniel M. Peeken, Jan C. Schnabel, Julia A. School of Computation Information and Technology Technical University Munich Germany Department of RadioOncology Klinikum rechts der Isar Technical University Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany Germany School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom

Pathological lymph node delineation is crucial in cancer diagnosis, progression assessment, and treatment planning. The MICCAI 2023 Lymph Node Quantification Challenge published the first public dataset for pathological lymph node segmentation in the mediastinum. As lymph node annotations are expensive, the challenge was formed as a weakly supervised learning task, where only a subset of all lymph nodes in the training set have been annotated. For the challenge submission, multiple methods for training on these weakly supervised data were explored, including noisy label training, loss masking of unlabeled data, and an approach that integrated the TotalSegmentator toolbox as a form of pseudo labeling in order to reduce the number of unknown voxels. Furthermore, multiple public TCIA datasets were incorporated into the training to improve the performance of the deep learning model. Our submitted model achieved a Dice score of 0.628 and an average symmetric surface distance of 5.8 mm on the challenge test set. With our submitted model, we accomplished third rank in the MICCAI2023 LNQ challenge. A finding of our analysis was that the integration of all visible, including non-pathological, lymph nodes improved the overall segmentation performance on pathological lymph nodes of the test set. Furthermore, segmentation models trained only on clinically enlarged lymph nodes, as given in the challenge scenario, could not generalize to smaller pathological lymph nodes. The code and model for the challenge submission are available at https://***/compai/MediastinalLymphNodeSegmentation. © 2024, CC BY.

关键词： Image segmentation

Differentially Private Active learning: Balancing Effective Data Selection and Privacy

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arXiv 2024年

作者： Schwethelm, Kristian Kaiser, Johannes Kuntzer, Jonas Yiǧitsoy, Mehmet Ruckert, Daniel Kaissis, Georgios Chair for Artificial Intelligence in Medicine Technical University of Munich Germany Deepc GmbH Munich Germany Department of Computing Imperial College London United Kingdom Institute for Machine Learning in Biomedical Imaging Helmholtz Munich Germany

Active learning (AL) is a widely used technique for optimizing data labeling in machine learning by iteratively selecting, labeling, and training on the most informative data. However, its integration with formal privacy-preserving methods, particularly differential privacy (DP), remains largely underexplored. While some works have explored differentially private AL for specialized scenarios like online learning, the fundamental challenge of combining AL with DP in standard learning settings has remained unaddressed, severely limiting AL's applicability in privacy-sensitive domains. This work addresses this gap by introducing differentially private active learning (DP-AL) for standard learning settings. We demonstrate that naively integrating DP-SGD training into AL presents substantial challenges in privacy budget allocation and data utilization. To overcome these challenges, we propose step amplification, which leverages individual sampling probabilities in batch creation to maximize data point participation in training steps, thus optimizing data utilization. Additionally, we investigate the effectiveness of various acquisition functions for data selection under privacy constraints, revealing that many commonly used functions become impractical. Our experiments on vision and natural language processing tasks show that DP-AL can improve performance for specific datasets and model architectures. However, our findings also highlight the limitations of AL in privacy-constrained environments, emphasizing the trade-offs between privacy, model accuracy, and data selection accuracy. © 2024, CC BY.

关键词： Active learning

Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging 21

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Efficient hierarchical Bayesian inference for spatio-tempora...

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Ali Hashemi Yijing Gao Chang Cai Sanjay Ghosh Klaus-Robert Müller Srikantan S. Nagarajan Stefan Haufe Uncertainty Inverse Modeling and Machine Learning Group Technische Universität Berlin Germany and Machine Learning Group Technische Universität Berlin Germany Department of Radiology and Biomedical Imaging University of California San Francisco Department of Radiology and Biomedical Imaging University of California San Francisco and National Engineering Research Center for E-Learning Central China Normal University China Machine Learning Group Technische Universität Berlin Germany and BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany and Department of Artificial Intelligence Korea University South Korea and Max Planck Institute for Informatics Saarbrücken Germany Uncertainty Inverse Modeling and Machine Learning Group Technische Universität Berlin Germany and Physikalisch-Technische Bundesanstalt Berlin Germany and Charité – Universitätsmedizin Berlin Germany and Bernstein Center for Computational Neuroscience Berlin Germany

ISBN: (纸本)9781713845393

Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models. Examples include M/EEG inverse problems, neural encoding models for task-based fMRI analyses, and climate science. In these domains, both the model parameters to be inferred and the measurement noise may exhibit a complex spatio-temporal structure. Existing work either neglects the temporal structure or leads to computationally demanding inference schemes. Overcoming these limitations, we devise a novel flexible hierarchical Bayesian framework within which the spatio-temporal dynamics of model parameters and noise are modeled to have Kronecker product covariance structure. Inference in our framework is based on majorization-minimization optimization and has guaranteed convergence properties. Our highly efficient algorithms exploit the intrinsic Riemannian geometry of temporal autocovariance matrices. For stationary dynamics described by Toeplitz matrices, the theory of circulant embeddings is employed. We prove convex bounding properties and derive update rules of the resulting algorithms. On both synthetic and real neural data from M/EEG, we demonstrate that our methods lead to improved performance.

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ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space

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arXiv 2023年

作者： Spieker, Veronika Huang, Wenqi Eichhorn, Hannah Stelter, Jonathan Weiss, Kilian Zimmer, Veronika A. Braren, Rickmer F. Karampinos, Dimitrios C. Hammernik, Kerstin Schnabel, Julia A. Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany School of Computation Information and Technology Technical University of Munich Germany School of Medicine Technical University of Munich Germany Philips GmbH Germany Partner Site Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom

Motion-resolved reconstruction for abdominal magnetic resonance imaging (MRI) remains a challenge due to the trade-off between residual motion blurring caused by discretized motion states and undersampling artefacts. In this work, we propose to generate blurring-free motion-resolved abdominal reconstructions by learning a neural implicit representation directly in k-space (NIK). Using measured sampling points and a data-derived respiratory navigator signal, we train a network to generate continuous signal values. To aid the regularization of sparsely sampled regions, we introduce an additional informed correction layer (ICo), which leverages information from neighboring regions to correct NIK’s prediction. Our proposed generative reconstruction methods, NIK and ICoNIK, outperform standard motion-resolved reconstruction techniques and provide a promising solution to address motion artefacts in abdominal MRI. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling

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arXiv 2024年

作者： Müller, Philip Meissen, Felix Kaissis, Georgios Rueckert, Daniel School of Computation Information and Technology TU Munich Garching85748 Germany The group for Reliable AI Institute for Machine Learning in Biomedical Imaging Helmholtz Munich Germany The Biomedical Image Analysis Group Imperial College London LondonSW7 2AZ United Kingdom

Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successes of multiple instance learning in this task for natural images, however, do not translate well to medical images due to the very different characteristics of their objects (i.e. pathologies). In this work, we propose Weakly Supervised ROI Proposal Networks (WSRPN), a new method for generating bounding box proposals on the fly using a specialized region of interest-attention (ROI-attention) module. WSRPN integrates well with classic backbone-head classification algorithms and is end-to-end trainable with only image-label supervision. We experimentally demonstrate that our new method outperforms existing methods in the challenging task of disease localization in chest X-ray images. Code: https://***/philip-mueller/wsrpn. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

From Model Based to Learned Regularization in Medical Image Registration: A Comprehensive Review

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arXiv 2024年

作者： Reithmeir, Anna Spieker, Veronika Sideri-Lampretsa, Vasiliki Rueckert, Daniel Schnabel, Julia A. Zimmer, Veronika A. Munich Germany Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Munich Germany Department of Computing Imperial College London London United Kingdom School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany

Image registration is fundamental in medical imaging applications, such as disease progression analysis or radiation therapy planning. The primary objective of image registration is to precisely capture the deformation between two or more images, typically achieved by minimizing an optimization problem. Due to its inherent ill-posedness, regularization is a key component in driving the solution toward anatomically meaningful deformations. A wide range of regularization methods has been proposed for both conventional and deep learning-based registration. However, the appropriate application of regularization techniques often depends on the specific registration problem, and no’one-fits-all’ method exists. Despite its importance, regularization is often overlooked or addressed with default approaches, assuming existing methods are sufficient. A comprehensive and structured review remains missing. This review addresses this gap by introducing a novel taxonomy that systematically categorizes the diverse range of proposed regularization methods. It highlights the emerging field of learned regularization, which leverages data-driven techniques to automatically derive deformation properties from the data. Moreover, this review examines the transfer of regularization methods from conventional to learning-based registration, identifies open challenges, and outlines future research directions. By emphasizing the critical role of regularization in image registration, we hope to inspire the research community to reconsider regularization strategies in modern registration algorithms and to explore this rapidly evolving field further. Copyright © 2024, The Authors. All rights reserved.

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