Ultrasound-CLIP: Semantic-Aware Contrastive Pre-training for Ultrasound Image-Text Understanding

Ultrasound imaging is widely used in clinical diagnostics due to its real-time capability and radiation-free nature. However, existing vision-language pre-training models, such as CLIP, are primarily designed for other modalities, and are difficult to directly apply to ultrasound data, which exhibit heterogeneous anatomical structures and diverse diagnostic attributes. To bridge this gap, we construct US-365K, a large-scale ultrasound image-text dataset containing 365k paired samples across 52 anatomical categories. We establish Ultrasonographic Diagnostic Taxonomy (UDT) containing two hierarchical knowledge frameworks. Ultrasonographic Hierarchical Anatomical Taxonomy standardizes anatomical organization, and Ultrasonographic Diagnostic Attribute Framework formalizes nine diagnostic dimensions, including body system, organ, diagnosis, shape, margins, echogenicity, internal characteristics, posterior acoustic phenomena, and vascularity. Building upon these foundations, we propose Ultrasound-CLIP, a semantic-aware contrastive learning framework that introduces semantic soft labels and semantic loss to refine sample discrimination. Moreover, we construct a heterogeneous graph modality derived from UDAF's textual representations, enabling structured reasoning over lesion-attribute relations. Extensive experiments with patient-level data splitting demonstrate that our approach achieves state-of-the-art performance on classification and retrieval benchmarks, while also delivering strong generalization to zero-shot, linear probing, and fine-tuning tasks.

MPCAR: Multi-Perspective Contextual Augmentation for Enhanced Visual Reasoning in Large Vision-Language Models

Despite significant advancements, Large Vision-Language Models (LVLMs) continue to face challenges in complex visual reasoning tasks that demand deep contextual understanding, multi-angle analysis, or meticulous detail recognition. Existing approaches often rely on single-shot image encoding and prompts, limiting their ability to fully capture nuanced visual information. Inspired by the notion that strategically generated "additional" information can serve as beneficial contextual augmentation, we propose Multi-Perspective Contextual Augmentation for Reasoning (MPCAR), a novel inference-time strategy designed to enhance LVLM performance. MPCAR operates in three stages: first, an LVLM generates N diverse and complementary descriptions or preliminary reasoning paths from various angles; second, these descriptions are intelligently integrated with the original question to construct a comprehensive context-augmented prompt; and finally, this enriched prompt guides the ultimate LVLM for deep reasoning and final answer generation. Crucially, MPCAR achieves these enhancements without requiring any fine-tuning of the underlying LVLM's parameters. Extensive experiments on challenging Visual Question Answering (VQA) datasets, including GQA, VQA-CP v2, and ScienceQA (Image-VQA), demonstrate that MPCAR consistently outperforms established baseline methods. Our quantitative results show significant accuracy gains, particularly on tasks requiring robust contextual understanding, while human evaluations confirm improved coherence and completeness of the generated answers. Ablation studies further highlight the importance of diverse prompt templates and the number of generated perspectives. This work underscores the efficacy of leveraging LVLMs' inherent generative capabilities to enrich input contexts, thereby unlocking their latent reasoning potential for complex multimodal tasks.