CausCLIP: Causality-Adapting Visual Scoring of Visual Language Models for Few-Shot Learning in Portable Echocardiography Quality Assessment

How do we transfer Vision Language Models (VLMs), pre-trained in the source domain of conventional echocardiography (Echo), to the target domain of few-shot portable Echo (fine-tuning)? Learning image causality is crucial for few-shot learning in portable echocardiography quality assessment (PEQA), due to the domain-invariant causal and topological consistency. However, the lack of significant domain shifts and well-labeled data in PEQA present challenges to get reliable measurements of image causality. We investigate the challenging problem of this task, i.e., learning a consistent representation of domain-invariant causal semantic features. We propose a novel VLMs based PEQA network, Causality-Adapting Visual Scoring CLIP (CausCLIP), embedding causal diposition to measure image causality for domain-invariant representation. Specifically, Causal-Aware Visual Adapter (CVA) identifies hidden asymmetric causal relationships and learns interpretable domain-invariant causal semantic consistency, thereby improving adaptability. Visual-Consistency Contrastive Learning (VCL) focuses on the most discriminative regions by registing visual-causal similarity, enhancing discriminability. Multi-granular Image-Text Adaptive Constraints (MAC) adaptively integrate task-specific semantic multi-granular information, enhancing robustness in multi-task learning. Experimental results show that CausCLIP outperforms state-of-the-art methods, achieving absolute improvements of 4.1 \(\%\) , 9.5 \(\%\) , and 8.5 \(\%\) in view category, quality score, and distortion metrics, respectively.