# Text Embedded Swin-UMamba for DeepLesion Segmentation

arXiv: https://arxiv.org/abs/2508.06453 · HTML: https://mlsanity.com/paper/2508.06453/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023)
- [Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining](https://mlsanity.com/paper/2402.03302/) (2024)
- [LViT: Language meets Vision Transformer in Medical Image Segmentation](https://mlsanity.com/paper/2206.14718/) (2022)
- [Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database](https://mlsanity.com/paper/1711.10535/) (2017)
- [Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports](https://mlsanity.com/paper/2306.08749/) (2023)
- [Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images](https://mlsanity.com/paper/2307.03942/) (2023)
- [The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography](https://mlsanity.com/paper/2406.05231/) (2024)
- [DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations](https://mlsanity.com/paper/1710.01766/) (2017)
- [Comparative Analysis of Pooling Mechanisms in LLMs: A Sentiment Analysis Perspective](https://mlsanity.com/paper/2411.14654/) (2024)
- [Universal Lesion Detection in CT Scans using Neural Network Ensembles](https://mlsanity.com/paper/2111.04886/) (2021)
- [R2Gen-Mamba: A Selective State Space Model for Radiology Report Generation](https://mlsanity.com/paper/2410.18135/) (2024)
- [Weakly-Supervised Detection of Bone Lesions in CT](https://mlsanity.com/paper/2402.00175/) (2024)
- [Longitudinal Assessment of Lung Lesion Burden in CT](https://mlsanity.com/paper/2504.06924/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Unified 2D Framework for DeepLesion Detection, Segmentation and Short Report Generation](https://mlsanity.com/paper/2608.02805/) (2026)

## Abstract (arXiv)

Segmentation of lesions on CT enables automatic measurement for clinical assessment of chronic diseases (e.g., lymphoma). Integrating large language models (LLMs) into the lesion segmentation workflow has the potential to combine imaging features with descriptions of lesion characteristics from the radiology reports. In this study, we investigate the feasibility of integrating text into the Swin-UMamba architecture for the task of lesion segmentation. The publicly available ULS23 DeepLesion dataset was used along with short-form descriptions of the findings from the reports. On the test dataset, our method achieved a high Dice score of 82.64, and a low Hausdorff distance of 6.34 pixels was obtained for lesion segmentation. The proposed Text-Swin-U/Mamba model outperformed prior approaches: 37.79% improvement over the LLM-driven LanGuideMedSeg model (p < 0.001), and surpassed the purely image-based XLSTM-UNet and nnUNet models by 2.58% and 1.01%, respectively. The dataset and code can be accessed at https://github.com/ruida/LLM-Swin-UMamba