  Liver cancer has a high incidence rate, but primary healthcare settings often
lack experienced doctors. Advances in large models and AI technologies offer
potential assistance. This work aims to address limitations in liver cancer
diagnosis models, such as poor understanding of medical images, insufficient
consideration of liver blood vessels, and ensuring accurate medical
information. We propose a specialized diagnostic assistant to improve the
diagnostic capabilities of less experienced doctors. Our framework combines
large and small models, using optimized small models for precise patient image
perception. Specifically, a segmentation network iteratively removes ambiguous
pixels for liver tumor segmentation, and a multi-scale, multi-level
differential network segments liver vessels. Features from these segmentations
and medical records form a patient's personalized knowledge base. For
diagnosis, Chain of Thought (COT) technology designs prompts mimicking
experienced doctors' thought patterns, and Retrieval-Augmented Generation (RAG)
technology provides answers based on reliable domain knowledge and trusted
cases. Our small model methods improve liver tumor and vessel segmentation
performance, resulting in more accurate information extraction. The large model
component scores over 1 point higher on a 10-point scale in evaluations by
doctors compared to control methods. Our method enhances semantic perception of
medical images, improves classification of ambiguous pixels, and optimizes
small object perception. It considers blood vessel positions for specific
treatments and improves response credibility and interpretability by mimicking
experienced doctors' thought processes using reliable resources. This approach
has been recognized by doctors and benefits liver cancer auxiliary diagnosis.
