With the development of multimodality and large language models, the deep learning-based technique for medical image captioning holds the potential to offer valuable diagnostic recommendations. However, current generic text and image pre-trained models do not yield satisfactory results when it comes to describing intricate details within medical images. In this paper, we present a novel medical image captioning method guided by the segment anything model (SAM) to enable enhanced encoding with both general and detailed feature extraction. In addition, our approach employs a distinctive pre-training strategy with mixed semantic learning to simultaneously capture both the overall information and finer details within medical images. We demonstrate the effectiveness of this approach, as it outperforms the pre-trained BLIP2 model on various evaluation metrics for generating descriptions of medical images.

翻译：随着多模态与大语言模型的发展，基于深度学习的医学图像描述生成技术具有提供诊断建议的潜力。然而，当前通用的文本与图像预训练模型在描述医学图像的复杂细节时效果不佳。本文提出一种由分割一切模型（SAM）引导的新型医学图像描述生成方法，通过通用特征与细节特征提取实现增强编码。此外，本方法采用独特的混合语义学习预训练策略，同步捕获医学图像的整体信息与微观细节。实验证明，该方法在生成医学图像描述的多个评估指标上均优于预训练BLIP2模型。