metadata
base_model:
- xinlai/LISA-7B-v1
pipeline_tag: image-text-to-text
ROSALIA-7B-v1
ROSALIA is a vision-language model (VLM) designed for precise lesion segmentation in chest X-rays (CXRs). It is a LISA model fine-tuned on the MIMIC-ILS dataset, a large-scale instruction-answer dataset for CXR lesion segmentation.
ROSALIA is capable of Instruction-Guided Lesion Segmentation (ILS), a medical-domain adaptation of referring image segmentation (RIS), allowing it to segment diverse lesions and provide textual explanations in response to simple, user-friendly instructions.
This model is the core checkpoint of the paper:
Instruction-Guided Lesion Segmentation for Chest X-rays with Automatically Generated Large-Scale Dataset, accepted to CVPR 2026.
- Code: https://github.com/checkoneee/ROSALIA
- Dataset: https://physionet.org/content/mimic-cxr-ext-ils/1.0.0/
- Paper: arXiv:2511.15186
📖 Citation
If you find this model or the related research useful, please cite our work:
@article{choi2025instruction,
title={Instruction-Guided Lesion Segmentation for Chest X-rays with Automatically Generated Large-Scale Dataset},
author={Choi, Geon and Yoon, Hangyul and Shin, Hyunju and Park, Hyunki and Seo, Sang Hoon and Yang, Eunho and Choi, Edward},
journal={arXiv preprint arXiv:2511.15186},
year={2025}
}