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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yenho Chen Noga Mudrik Kyle A. Johnsen Sankaraleengam Alagapan Adam S. Charles Christopher J. Rozell Machine Learning Center Georgia Institute of Technology and Coulter Dept. of Biomedical Engineering Emory University and Georgia Institute of Technology Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University Coulter Dept. of Biomedical Engineering Emory University and Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology Machine Learning Center Georgia Institute of Technology and School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (纸本)9798331314385

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. Code is available at: https://***/siplab-gt/probabilistic-decomposed-linear-dynamical-systems

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Cross-Domain and Cross-Dimension learning for Image-to-Graph Transformers

Cross-Domain and Cross-Dimension Learning for Image-to-Graph...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Berger, Alexander H. Lux, Laurin Shit, Suprosanna Ezhov, Ivan Kaissis, Georgios Menten, Martin J. Rueckert, Daniel Paetzold, Johannes C. School of Computation Information and Technology Technical University of Munich Germany Department of Computing Imperial College London United Kingdom Munich Germany Institute for Machine Learning in Biomedical Imaging Helmholtz Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Weill Cornell Medicine Cornell University New York City United States

ISBN: (纸本)9798331510831

Direct image-to-graph transformation is a challenging task that involves solving object detection and relationship prediction in a single model. Due to this task's complexity, large training datasets are rare in many domains, making the training of deep-learning methods challenging. This data sparsity necessitates transfer learning strategies akin to the state-of-the-art in general computer vision. In this work, we introduce a set of methods enabling cross-domain and cross-dimension learning for image-to-graph trans-formers. We propose (1) a regularized edge sampling loss to effectively learn object relations in multiple domains with different numbers of edges, (2) a domain adaptation frame-work for image-to-graph transformers aligning image- and graph-level features from different domains, and (3) a projection function that allows using 2D data for training 3D transformers. We demonstrate our method's utility in cross-domain and cross-dimension experiments, where we utilize labeled data from 2D road networks for simultaneous learning in vastly different target domains. Our method consistently outperforms standard transfer learning and self-supervised pretraining on challenging benchmarks, such as retinal or whole-brain vessel graph extraction.11Code: ***/AlexanderHBerger/cross-dim_i2g © 2025 IEEE.

关键词： Self-supervised learning

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Interpretable Representation learning of Cardiac MRI via Attribute Regularization

arXiv

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arXiv 2024年

作者： Di Folco, Maxime Bercea, Cosmin I. Chan, Emily Schnabel, Julia A. Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany Helmholtz AI Helmholtz Munich Germany School of Computation Information and Technology Technical University of Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom

Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models. Several approaches have been recently considered to encode attributes in the latent space to enhance its interpretability. Notably, attribute regularization aims to encode a set of attributes along the dimensions of a latent representation. However, this approach is based on Variational AutoEncoder and suffers from blurry reconstruction. In this paper, we propose an Attributed-regularized Soft Introspective Variational Autoencoder that combines attribute regularization of the latent space within the framework of an adversarially trained variational autoencoder. We demonstrate on short-axis cardiac Magnetic Resonance images of the UK Biobank the ability of the proposed method to address blurry reconstruction issues of variational autoencoder methods while preserving the latent space interpretability. © 2024, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration

arXiv

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arXiv 2024年

作者： Reithmeir, Anna Felsner, Lina Braren, Rickmer Schnabel, Julia A. Zimmer, Veronika A. School of Computation Information and Technology Technical University of Munich Germany Munich Center for Machine Learning Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany School of Biomedical Engineering and Imaging Sciences King's College London United Kingdom School of Medicine & Health Klinkum Rechts der Isar Technical University of Munich Germany Partner Site Munich Germany

Physics-inspired regularization is desired for intra-patient image registration since it can effectively capture the biomechanical characteristics of anatomical structures. However, a major challenge lies in the reliance on physical parameters: Parameter estimations vary widely across the literature, and the physical properties themselves are inherently subject-specific. In this work, we introduce a novel data-driven method that leverages hypernetworks to learn the tissue-dependent elasticity parameters of an elastic regularizer. Notably, our approach facilitates the estimation of patient-specific parameters without the need to retrain the network. We evaluate our method on three publicly available 2D and 3D lung CT and cardiac MR datasets. We find that with our proposed subject-specific tissue-dependent regularization, a higher registration quality is achieved across all datasets compared to using a global regularizer. The code is available at https://***/compai-lab/2024-miccai-reithmeir. © 2024, CC BY.

关键词： Tissue

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Differentially Private Active learning: Balancing Effective Data Selection and Privacy

Differentially Private Active Learning: Balancing Effective ...

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Secure and Trustworthy machine learning (SaTML), IEEE Conference on

作者： Kristian Schwethelm Johannes Kaiser Jonas Kuntzer Mehmet Yiğitsoy Daniel Rückert Georgios Kaissis Chair for AI in Healthcare and Medicine Technical University of Munich (TUM) and TUM University Hospital Germany deepc GmbH Munich Germany Department of Computing Imperial College London UK Munich Center for Machine Learning (MCML) Germany Institute for Machine Learning in Biomedical Imaging Helmholtz Munich Germany

ISBN: (数字)9798331517113

ISBN: (纸本)9798331517120

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.

关键词： Training Privacy Differential privacy Accuracy Active learning machine learning Natural language processing Labeling Resource management Standards

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

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

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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

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

arXiv

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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

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

arXiv

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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

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Attribute Regularized Soft Introspective VAE: Towards Cardiac Attribute Regularization Through MRI Domains

arXiv

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arXiv 2023年

作者： Folco, Maxime Di Bercea, Cosmin I. Schnabel, Julia A. Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Neuherberg Germany Technical University of Munich Munich Germany King’s College London London United Kingdom

Deep generative models have emerged as influential instruments for data generation and manipulation. Enhancing the controllability of these models by selectively modifying data attributes has been a recent focus. Variational Autoencoders (VAEs) have shown promise in capturing hidden attributes but often produce blurry reconstructions. Controlling these attributes through different imaging domains is difficult in medical imaging. Recently, Soft Introspective VAE leverage the benefits of both VAEs and Generative Adversarial Networks (GANs), which have demonstrated impressive image synthesis capabilities, by incorporating an adversarial loss into VAE training. In this work, we propose the Attributed Soft Introspective VAE (Attri-SIVAE) by incorporating an attribute regularized loss, into the Soft-Intro VAE framework. We evaluate experimentally the proposed method on cardiac MRI data from different domains, such as various scanner vendors and acquisition centers. The proposed method achieves similar performance in terms of reconstruction and regularization compared to the state-of-the-art Attributed regularized VAE but additionally also succeeds in keeping the same regularization level when tested on a different dataset, unlike the compared method. Code: ************************* © 2023, CC BY-NC-ND.

关键词： Medical imaging

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Sparse annotation strategies for segmentation of short axis cardiac MRI

arXiv

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arXiv 2023年

作者： Stein, Josh Folco, Maxime Di Schnabel, Julia A. Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Neuherberg Germany Technical University of Munich Munich Germany King’s College London London United Kingdom

Short axis cardiac MRI segmentation is a well-researched topic, with excellent results achieved by state-of-the-art models in a supervised setting. However, annotating MRI volumes is time-consuming and expensive. Many different approaches (e.g. transfer learning, data augmentation, few-shot learning, etc.) have emerged in an effort to use fewer annotated data and still achieve similar performance as a fully supervised model. Nevertheless, to the best of our knowledge, none of these works focus on which slices of MRI volumes are most important to annotate for yielding the best segmentation results. In this paper, we investigate the effects of training with sparse volumes, i.e. reducing the number of cases annotated, and sparse annotations, i.e. reducing the number of slices annotated per case. We evaluate the segmentation performance using the state-of-the-art nnU-Net model on two public datasets to identify which slices are the most important to annotate. We have shown that training on a significantly reduced dataset (48 annotated volumes) can give a Dice score greater than 0.85 and results comparable to using the full dataset (160 and 240 volumes for each dataset respectively). In general, training on more slice annotations provides more valuable information compared to training on more volumes. Further, annotating slices from the middle of volumes yields the most beneficial results in terms of segmentation performance, and the apical region the worst. When evaluating the trade-off between annotating volumes against slices, annotating as many slices as possible instead of annotating more volumes is a better strategy. © 2023, CC BY-NC-SA.

关键词： Economic and social effects

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