A Novel Approach for Stratifying Pulmonary Edema Severity on Chest X-ray via Dual-Mechanic Self-Learning and Bidirectional Multi-Modal Cross-Attention Algorithms

Abstract

Abstract Accurate assessment of pulmonary edema severity in acute decompensated congestive heart failure (CHF) patients is vital for treatment decisions. Traditional methods face challenges due to the complexity of chest X-ray (CXR) and unstructured radiology reports. We proposed a method combining self-supervised learning and multimodal cross-attention to address these challenges. Dual-mechanic self-supervised pre-training enhances feature extraction using contrastive learning between text and image features, and generative learning between images. A bidirectional multi-modal cross-attention model integrates image and text information for fine-tuning, improving model performance. Four CXR datasets consisting of 519, 437 images were used for pre-training; 1200 randomly selected image-text pairs were used for fine-tuning and partitioned into train, validation, and test sets at 3: 1: 1. Ablation studies for pre-training and fine-tuning approaches indicated their practicality as evidenced by the optimal macro F1 score of 0.667 and optimal macro-AUC of 0.904. It also outperformed other state-of-the-art multi-modality methods. The novel approach could accurately assess pulmonary edema severity, offering crucial support for CHF patient management.