  In this paper, we present our champion solution to the Global Artificial
Intelligence Technology Innovation Competition Track 1: Medical Imaging
Diagnosis Report Generation. We select CPT-BASE as our base model for the text
generation task. During the pre-training stage, we delete the mask language
modeling task of CPT-BASE and instead reconstruct the vocabulary, adopting a
span mask strategy and gradually increasing the number of masking ratios to
perform the denoising auto-encoder pre-training task. In the fine-tuning stage,
we design iterative retrieval augmentation and noise-aware similarity bucket
prompt strategies. The retrieval augmentation constructs a mini-knowledge base,
enriching the input information of the model, while the similarity bucket
further perceives the noise information within the mini-knowledge base, guiding
the model to generate higher-quality diagnostic reports based on the similarity
prompts. Surprisingly, our single model has achieved a score of 2.321 on
leaderboard A, and the multiple model fusion scores are 2.362 and 2.320 on the
A and B leaderboards respectively, securing first place in the rankings.
