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

arXiv: https://arxiv.org/abs/2410.18135 · HTML: https://mlsanity.com/paper/2410.18135/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Mamba for medical image analysis](https://mlsanity.com/ssm/mamba-for-medical-image-analysis/)

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [Generating Radiology Reports via Memory-driven Transformer](https://mlsanity.com/paper/2010.16056/) (2020)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [Cross-modal Memory Networks for Radiology Report Generation](https://mlsanity.com/paper/2204.13258/) (2022)

## Built on by (most cited first, 8 of 8)

- [Anatomical Attention Alignment representation for Radiology Report Generation](https://mlsanity.com/paper/2505.07689/) (2025)
- [Text Embedded Swin-UMamba for DeepLesion Segmentation](https://mlsanity.com/paper/2508.06453/) (2025)
- [SliceWorld: A Predictive and Controllable World-State Model for CT Report Generation](https://mlsanity.com/paper/2605.24371/) (2026)
- [LLaMA-XR: A Novel Framework for Radiology Report Generation using LLaMA and QLoRA Fine Tuning](https://mlsanity.com/paper/2506.03178/) (2025)
- [Transformers in Medicine: Improving Vision-Language Alignment for Medical Image Captioning](https://mlsanity.com/paper/2510.25164/) (2025)
- [TAVR-VLM: Risk-Conditioned Causal Grounding for Hallucination-Resistant Report Generation](https://mlsanity.com/paper/2606.26874/) (2026)
- [A Unified 2D Framework for DeepLesion Detection, Segmentation and Short Report Generation](https://mlsanity.com/paper/2608.02805/) (2026)
- [Pre-training, Reasoning, Benchmarking: X-ray Report Generation on CheXpert Plus Dataset](https://mlsanity.com/paper/2610.08813/) (2026)

## Abstract (arXiv)

Radiology report generation is crucial in medical imaging,but the manual annotation process by physicians is time-consuming and labor-intensive, necessitating the develop-ment of automatic report generation methods. Existingresearch predominantly utilizes Transformers to generateradiology reports, which can be computationally intensive,limiting their use in real applications. In this work, we presentR2Gen-Mamba, a novel automatic radiology report genera-tion method that leverages the efficient sequence processingof the Mamba with the contextual benefits of Transformerarchitectures. Due to lower computational complexity ofMamba, R2Gen-Mamba not only enhances training and in-ference efficiency but also produces high-quality reports.Experimental results on two benchmark datasets with morethan 210,000 X-ray image-report pairs demonstrate the ef-fectiveness of R2Gen-Mamba regarding report quality andcomputational efficiency compared with several state-of-the-art methods. The source code can be accessed online.