FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions
Department of Mechanical Engineering, Stanford University（斯坦福大学机械工程系）；Institute for Computational and Mathematical Engineering, Stanford University（斯坦福大学计算与数学工程研究所）；Bioengineering, Stanford University（斯坦福大学生物工程系）；Pediatric Cardiology, Stanford University（斯坦福大学儿科心脏病学）；Department of Applied and Computational Mathematics and Statistics, University of Notre Dame（诺丁汉大学应用与计算数学与统计学系）
Chloe H. Choi, Alison L. Marsden, Daniele E. Schiavazzi
Boundary condition tuning is a fundamental step in patient-specific cardiovascular modeling. Despite an increase in offline training cost, recent methods in data-driven variational inference can efficiently estimate the joint posterior distribution of boundary conditions, with amortization of training efforts over clinical targets. However, even the most modern approaches fall short in two important scenarios: open-loop models with known mean flow and assumed waveform shapes, and anatomies affected by vascular lesions where segmentation influences the reachability of pressure or flow split targets. In both cases, boundary conditions cannot be tuned in isolation. We introduce a general amortized inference framework based on probabilistic flow that treats clinical targets, inflow features, and point cloud embeddings of patient-specific anatomies as either conditioning variables or quantities to be jointly estimated. We demonstrate the approach on two patient-specific models: an aorto-iliac bifurcation with varying stenosis locations and severity, and a coronary arterial tree.
Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation
Department of Statistics, University of Oxford, Oxford, United Kingdom（英国牛津大学统计系）；Department of Statistics, University of Auckland, Auckland, New Zealand（新西兰奥克兰大学统计系）
Amortized Phylodynamic Inference with Neural Bayes Estimators and Recursive Neural Networks
MRC Biostatistics Unit, University of Cambridge（剑桥大学生物统计单元）；ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems, The University of Melbourne（墨尔本大学细胞系统数学分析卓越中心）；School of Mathematics and Statistics, The University of Melbourne（墨尔本大学数学与统计学院）；Department of Biosystems Science and Engineering, ETH Zürich（苏黎世联邦理工学院生物系统科学与工程系）；Swiss Institute of Bioinformatics (SIB)（瑞士生物信息学研究所）
Environment-Robust Representation Learning with Empirical Bayes
Department of Statistics Columbia University（统计学系哥伦比亚大学）；Columbia University（哥伦比亚大学）；Departments of Statistics and Computer Science Columbia University（统计学与计算机科学系哥伦比亚大学）
In-Context Batch Black-box Optimization via Flow Matching
ELLIS Institute Finland（芬兰ELLIS研究所）；Department of Computer Science, Aalto University（阿尔托大学计算机系）；Department of Computer Science, University of Manchester（曼彻斯特大学计算机系）
Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification
School of Information Technology, Monash University Malaysia（墨尔本大学马来西亚分校信息科技学院）；School of Engineering, Monash University Malaysia（墨尔本大学马来西亚分校工程学院）；Statistics Program, King Abdullah University of Science and Technology（国王阿卜杜勒·阿齐兹大学科学与技术学院统计学项目）
Scalable Simulation-Based Model Inference with Test-Time Complexity Control
Machine Learning in Science, University of Tübingen；Tübingen AI Center, Tübingen, Germany；Max Planck Institute for Intelligent Systems, Department Empirical Inference, Tübingen, Germany；Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK；National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, United Kingdom
Flexible Amortized Variational Inference in qBOLD MRI
University of Sussex（萨塞克斯大学）；Brighton and Sussex Medical School（布莱顿和萨塞克斯医学院）；St. Vincent’s University Hospital（圣文森特大学医院）；University of Nottingham（诺丁汉大学）；Cardiff University（卡迪夫大学）
Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring
University of Edinburgh（爱丁堡大学）
提出了一种应用于重症监护病房（ICU）病人监护的判别切换线性动态系统（DSLDS）。我们的方法是基于识别的健康状态的患者，他们观察到的生命体征使用判别分类器，然后推断出他们的基础生理值的条件下，这种状态。这项工作建立在阶乘切换线性动态系统（FSLDS）（Quinn et al.，2009年，曾在类似的环境中使用过。FSLDS是一个生成模型，而DSLDS是一个判别模型。我们在两个真实世界的数据集上证明，DSLDS在大多数感兴趣的情况下都能够优于FSLDS，并且两个模型的$α$混合物比两个模型中的任何一个单独实现更高的性能。
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders
Georgia Institute of Technology（佐治亚理工学院）；IBM Research（IBM研究院）；Vanderbilt University（范德堡大学）；Sutter Health（萨特健康）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
PG-LRF: Physiology-Guided Latent Rectified Flow for Electro-Hemodynamic PPG-to-ECG Generation
Department of Computer Science, Emory University（埃默里大学计算机科学系）；Nell Hodgson Woodruff School of Nursing, Emory University（埃默里大学Nell Hodgson Woodruff护理学院）；Department of Computer Science, University of California, Los Angeles（加州大学洛杉矶分校计算机科学系）；Department of Computer Science, University of Southern California（南加州大学计算机科学系）；Department of Statistics, University of Wisconsin–Madison（威斯康星大学麦迪逊分校统计系）
