  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).
