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arXiv · 2407.01271

First Place Solution of 2023 Global Artificial Intelligence Technology Innovation Competition Track 1

Xiangyu Wu·Hailiang Zhang·Yang Yang·Jianfeng Lu
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Abstract

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.

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Xiangyu Wu, Hailiang Zhang, Yang Yang, Jianfeng Lu. 2024-07-04. First Place Solution of 2023 Global Artificial Intelligence Technology Innovation Competition Track 1. https://arxiv.org/abs/2407.01271

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