Patient-specific cardiac computational models are essential for the efficient realization of precision medicine and in-silico clinical trials using digital twins. Cardiac digital twins can provide non-invasive characterizations of cardiac functions for individual patients, and therefore are promising for the patient-specific diagnosis and therapy stratification. However, current workflows for both the anatomical and functional twinning phases, referring to the inference of model anatomy and parameter from clinical data, are not sufficiently efficient, robust, and accurate. In this work, we propose a deep learning based patient-specific computational model, which can fuse both anatomical and electrophysiological information for the inference of ventricular activation properties, i.e., conduction velocities and root nodes. The activation properties can provide a quantitative assessment of cardiac electrophysiological function for the guidance of interventional procedures. We employ the Eikonal model to generate simulated electrocardiogram (ECG) with ground truth properties to train the inference model, where specific patient information has also been considered. For evaluation, we test the model on the simulated data and obtain generally promising results with fast computational time.
PYRA: Parallel Yielding Re-Activation for Training-Inference Efficient Task Adaptation
School of Software, Tsinghua University（清华大学软件学院）；BNRist, Tsinghua University（清华大学北京信息科学与技术国家研究中心）；Department of Automation, Tsinghua University（清华大学自动化系）
Deep Learning-based Prediction of Electrical Arrhythmia Circuits from Cardiac Motion: An In-Silico Study
Cardiovascular Research Institute, University of California, San Francisco（加州大学旧金山分校心血管研究所）；University of California, Berkeley（加州大学伯克利分校）；College of Engineering, Michigan State University（密歇根州立大学工程学院）；Division of Cardiology, University of California, San Francisco（加州大学旧金山分校心脏病学系）
Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference
Shandong University（山东大学）；Institute of Tsinghua-Berkeley Shenzhen, Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院清华-伯克利深圳学院）；Hangzhou Dianzi University（杭州电子科技大学）；Shenzhen ZNV Technology Co., Ltd（深圳中兴网信科技有限公司）；Shenzhen Graduate School of Harbin Institute of Technology（哈尔滨工业大学深圳研究生院）；Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院生物医药与健康工程研究院）
Translating Inference-Time Control to Radiology Vision-Language Models: Activation Steering for Pneumonia Classification on Chest X-rays
Universidade Federal de São Paulo (UNIFESP)（圣保罗联邦大学）；Hospital Israelita Albert Einstein（以色列阿尔伯特·爱因斯坦医院）；Stanford University School of Medicine（斯坦福大学医学院）；DASA；University of Texas Southwestern Medical Center (UTSW)（德克萨斯大学西南医学中心）；Eden
