基于语言大模型与书籍知识的肾脏数字病理视觉知识搜索平台
Renal digital pathology visual knowledge search platform based on language large model and book knowledge
AI总结：
本文针对大模型在数字病理学中的应用探索，基于60本肾脏病理学书籍构建了包含10,317张图像及文本描述的知识库，评估了多个大模型的特征能力，并开发了名为RppD的语义检索系统。
摘要：
大模型已成为主流，但其在数字病理学中的应用仍需探索。同时，肾脏病理图像在肾脏疾病诊断中发挥重要作用。我们基于60本肾脏病理学书籍进行图像分割并配对相应的文本描述，利用大模型对所有图像和文本描述特征进行聚类分析，最终构建了基于大模型语义特征的检索系统。基于上述分析，我们建立了包含10,317张肾脏病理图像及配对文本描述的知识库，随后评估了包括GPT2、gemma、LLma和Qwen在内的4个大模型的语义特征能力，以及dinov2大模型的图像特征能力。此外，我们构建了一个基于文本描述检索病理图像的语义检索系统，并将其命名为RppD（aidp.zjsru.edu.cn）。
英文摘要：
Large models have become mainstream, yet their applications in digital pathology still require exploration. Meanwhile renal pathology images play an important role in the diagnosis of renal diseases. We conducted image segmentation and paired corresponding text descriptions based on 60 books for renal pathology, clustering analysis for all image and text description features based on large models, ultimately building a retrieval system based on the semantic features of large models. Based above analysis, we established a knowledge base of 10,317 renal pathology images and paired corresponding text descriptions, and then we evaluated the semantic feature capabilities of 4 large models, including GPT2, gemma, LLma and Qwen, and the image-based feature capabilities of dinov2 large model. Furthermore, we built a semantic retrieval system to retrieve pathological images based on text descriptions, and named RppD (aidp.zjsru.edu.cn).
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。
