Renal digital pathology visual knowledge search platform based on language large model and book knowledge

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). Key Words: large model, renal pathology, renal knowledge base, semantic features Introduction Histopathology holds a preeminent position within the diagnostic framework of a multitude of renal afflictions[1], including Acute kidney injury[2] to chronic glomerular inflammation[3, 4], renal organ transplantation[5], and renal malignancies[6] etc. Given the pivotal role that histopathological analysis plays in informing therapeutic strategies and prognostic assessments, seasoned investigators and clinicians have devoted substantial efforts to compile exhaustive book of prototypical histological samples, documenting the hallmark histopathological hallmarks distinctive to each disease phenotype. While numerous books provide a wealth of cases for study and research, readers often lack the capability to promptly retrieve relevant images for real-time clinical cases to give a precise diagnosis in practical diagnostic process. The advent of large language models has revolutionized the rapid construction and retrieval of knowledge bases, offering a more efficient approach.