R2Gen-Mamba: A Selective State Space Model for Radiology Report Generation

Yongheng Sun, Yueh Z. Lee, Genevieve A. Woodard, Hongtu Zhu, Chunfeng Lian, Mingxia Liu

Abstract: Radiology report generation is crucial in medical imaging, but the manual annotation process by physicians is time-consuming and labor-intensive, necessitating the development of automatic report generation methods. Existing research predominantly utilizes Transformers to generate radiology reports, which can be computationally intensive, limiting their use in real applications. In this work, we present R2Gen-Mamba, a novel automatic radiology report generation method that leverages the efficient sequence processing of the Mamba with the contextual benefits of Transformer architectures. Due to lower computational complexity of Mamba, R2Gen-Mamba not only enhances training and inference efficiency but also produces high-quality reports. Experimental results on two benchmark datasets with more than 210,000 radiograph-report pairs demonstrate the effectiveness of R2Gen-Mamba regarding report quality and computational efficiency compared with several state-of-the-art methods. The source code can be accessed online.