TriDP-PTM: a three-stage distortion-perception tradeoff guides the pre-training model for radar cardiac sensing 文章

ArXiv CS.CV2026-05-26NEWSen作者: Jinye Li (National Institute of Health Data Science, Peking University, Beijing, China, Institute of Medical Technology, Peking University, Beijing, China, Beijing University of Posts and Telecommunications, Beijing, China), Aidong Men (School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China), Yang Liu (Wangxuan Institute of Computer Technology, Peking University, Beijing, China), Qingchao Chen (National Institute of Health Data Science, Peking University, Beijing, China, Institute of Medical Technology, Peking University, Beijing, China, State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China)

摘要

arXiv:2605.25725v1 Announce Type: new Abstract: Cardiovascular diseases (CVDs) remain a leading cause of death globally, necessitating continuous, accurate non-invasive cardiac monitoring. While non-contact radar-based approaches show great promise, they often employ a single "distortion-driven" or "perception-driven" paradigm, frequently facing a trade-off between "low distortion but weak semantic information" and "high perceptual fidelity but poor interpretability." To address this, we propose a Three-stage Distortion-Perception Pre-Training Model (TriDP-PTM), a radar-based multi-scale fusion dual-path framework that systematically compares the "direct radar-to-task" path against an "indirect radar-to-ECG-to-task" path. By integrating an ECG generator with a feature discriminator to form a composite loss function, our approach effectively incorporates medical priors - such as ECG morphology and rhythm - into downstream tasks.

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