LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report Generation

• URL: http://arxiv.org/abs/2411.16523v1
• Date: Mon, 25 Nov 2024 16:10:05 GMT
• Title: LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report Generation
• Authors: Steven Song, Anirudh Subramanyam, Irene Madejski, Robert L. Grossman,
• Abstract summary: We propose Label Boosted Retrieval Augmented Generation (LaB-RAG) to generate radiology reports. We show that LaB-RAG achieves better results across natural language and radiology language metrics compared with other retrieval-based RRG methods. We critique the use of a popular RRG metric, arguing it is possible to artificially inflate its results without true data-leakage.
• Score: 1.1029725477806065
• License:
• Abstract: In the current paradigm of image captioning, deep learning models are trained to generate text from image embeddings of latent features. We challenge the assumption that these latent features ought to be high-dimensional vectors which require model fine tuning to handle. Here we propose Label Boosted Retrieval Augmented Generation (LaB-RAG), a text-based approach to image captioning that leverages image descriptors in the form of categorical labels to boost standard retrieval augmented generation (RAG) with pretrained large language models (LLMs). We study our method in the context of radiology report generation (RRG), where the task is to generate a clinician's report detailing their observations from a set of radiological images, such as X-rays. We argue that simple linear classifiers over extracted image embeddings can effectively transform X-rays into text-space as radiology-specific labels. In combination with standard RAG, we show that these derived text labels can be used with general-domain LLMs to generate radiology reports. Without ever training our generative language model or image feature encoder models, and without ever directly "showing" the LLM an X-ray, we demonstrate that LaB-RAG achieves better results across natural language and radiology language metrics compared with other retrieval-based RRG methods, while attaining competitive results compared to other fine-tuned vision-language RRG models. We further present results of our experiments with various components of LaB-RAG to better understand our method. Finally, we critique the use of a popular RRG metric, arguing it is possible to artificially inflate its results without true data-leakage.

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