最新研究提出多标簽半監督統一框架解決标簽稀缺、多種心血管疾病并發等問題

來自香港城市大學的研究人員面向心血管疾病預測提出了一種新的多标簽半監督框架。心電圖（Electrocardiography, ECG）是預測心血管疾病（CVDs）的無創工具。由于深度學習技術的快速發展，目前基于心電圖的診斷系統表現出良好的性能。然而，标簽稀缺問題、多種心血管疾病并發以及在未見數據集上表現不佳極大地阻礙了深度學習模型的廣泛應用。在統一框架中解決以上問題仍然是一個重大挑戰。爲此，本文提出了一種多标簽半監督模型（ECGMatch），在有限的監督信息下同時識别多種CVD。在 ECGMatch 中，作者開發了一個 ECGAugment 模塊，用于弱、強心電圖數據增強，爲模型訓練生成不同的樣本。随後，作者還設計了一個超參數高效框架，用于生成和完善僞标簽，從而緩解标簽稀缺問題。最後，還提出了一個标簽相關性對齊模塊，用于捕捉已标注樣本中不同 CVD 的共現信息，并将這些信息傳播到未标注樣本中。在四個數據集和三個協議上進行的廣泛實驗證明了所提模型的有效性和穩定性。相關研究成果于2023年12月14日發表在《模式分析與機器智能》（IEEE Transactions on Pattern Analysis and Machine Intelligence）期刊。

Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study

Rushuang Zhou, Lei Lu, Zijun Liu, et al.（Department of Biomedical Engineering, City university of Hong Kong, Hong Kong, China , Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China）

TPAMI

Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models. Addressing them in a unified framework remains a significant challenge. To this end, we propose a multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision. In the ECGMatch, an ECGAugment module is developed for weak and strong ECG data augmentation, which generates diverse samples for model training. Subsequently, a hyperparameter-efficient framework with neighbor agreement modeling and knowledge distillation is designed for pseudo-label generation and refinement, which mitigates the label scarcity problem. Finally, a label correlation alignment module is proposed to capture the co-occurrence information of different CVDs within labeled samples and propagate this information to unlabeled samples. Extensive experiments on four datasets and three protocols demonstrate the effectiveness and stability of the proposed model, especially on unseen datasets. As such, this model can pave the way for diagnostic systems that achieve robust performance on multi-label CVDs prediction with limited supervision. Code is available at https://github.com/KAZABANA/ECGMatch.