用于医学图像描述的Sam引导增强细粒度编码与混合语义学习
Sam-Guided Enhanced Fine-Grained Encoding with Mixed Semantic Learning for Medical Image Captioning
AI总结：
本文提出一种由SAM引导的医学图像描述新方法，结合混合语义学习预训练策略实现增强的细粒度特征编码，在多项评估指标上优于BLIP2模型。
摘要：
随着多模态和大语言模型的发展，基于深度学习的医学图像描述技术有望提供有价值的诊断建议。然而，当前的通用文本和图像预训练模型在描述医学图像内部的复杂细节时，效果并不令人满意。在本文中，我们提出了一种由Segment Anything Model（SAM）引导的新型医学图像描述方法，通过通用和详细特征提取实现增强编码。此外，我们的方法采用了一种独特的预训练策略，结合混合语义学习，以同时捕获医学图像中的整体信息和更细微的细节。我们证明了该方法的有效性，它在生成医学图像描述的各种评估指标上均优于预训练的BLIP2模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
