MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware Multimodal Large Language Models

• URL: http://arxiv.org/abs/2411.11362v1
• Date: Mon, 18 Nov 2024 08:13:22 GMT
• Title: MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware Multimodal Large Language Models
• Authors: Harshita Sharma, Valentina Salvatelli, Shaury Srivastav, Kenza Bouzid, Shruthi Bannur, Daniel C. Castro, Maximilian Ilse, Sam Bond-Taylor, Mercy Prasanna Ranjit, Fabian Falck, Fernando Pérez-García, Anton Schwaighofer, Hannah Richardson, Maria Teodora Wetscherek, Stephanie L. Hyland, Javier Alvarez-Valle,
• Abstract summary: We introduce MAIRA-Seg, a segmentation-aware MLLM framework for radiology report generation. We train expert segmentation models to obtain mask pseudolabels for radiology-specific structures in CXRs. We employ mask-aware prompting to generate draft radiology reports.
• Score: 36.59952396405939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is growing interest in applying AI to radiology report generation, particularly for chest X-rays (CXRs). This paper investigates whether incorporating pixel-level information through segmentation masks can improve fine-grained image interpretation of multimodal large language models (MLLMs) for radiology report generation. We introduce MAIRA-Seg, a segmentation-aware MLLM framework designed to utilize semantic segmentation masks alongside CXRs for generating radiology reports. We train expert segmentation models to obtain mask pseudolabels for radiology-specific structures in CXRs. Subsequently, building on the architectures of MAIRA, a CXR-specialised model for report generation, we integrate a trainable segmentation tokens extractor that leverages these mask pseudolabels, and employ mask-aware prompting to generate draft radiology reports. Our experiments on the publicly available MIMIC-CXR dataset show that MAIRA-Seg outperforms non-segmentation baselines. We also investigate set-of-marks prompting with MAIRA and find that MAIRA-Seg consistently demonstrates comparable or superior performance. The results confirm that using segmentation masks enhances the nuanced reasoning of MLLMs, potentially contributing to better clinical outcomes.

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