Vision-Language Models (VLMs) are a new class of artificial intelligence systems that combine image analysis with natural language understanding. These models are trained on large datasets of paired images and text, enabling them to interpret medical images while also generating, understanding, or responding to clinical narratives. In medical imaging, VLMs offer transformative potential. They can automate complex tasks such as image captioning, report generation, abnormality detection, and cross-modal retrieval (e.g., “find images like this with similar diagnoses”). By linking visual features to clinical language, VLMs support more explainable and interactive AI tools that improve radiology workflows, reduce diagnostic errors, and enhance medical education.
An Efficient Vision Language Model For 3D Medical Image Analysis
Med3DVLM captures fine-grained spatial features using decomposed 3D convolutions, and SigLIP Contrastive Learning Strategy to improve image-text alignment.
Efficient 3D Vision Language Modeling with Decomposed Convolutions
DCFormer is integrated into a CLIP-based vision-language framework, enabling efficient alignment of 3D medical images with radiology reports.
