MieDB-100k: A Comprehensive Dataset for Medical Image Editing
The scarcity of high-quality data remains a primary bottleneck in adapting multimodal generative models for medical image editing. Existing medical image editing datasets often suffer from limited diversity, neglect of medical image understanding and inability to balance quality with scalability. To address these gaps, we propose MieDB-100k, a large-scale, high-quality and diverse dataset for text-guided medical image editing. It categorizes editing tasks into perspectives of Perception, Modification and Transformation, considering both understanding and generation abilities. We construct MieDB-100k via a data curation pipeline leveraging both modality-specific expert models and rule-base...
| Authors | Yongfan Lai, Wen Qian, Bo Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 February 2026 |
| Last updated at source | 12 May 2026 |
Models and datasets referencing this paper
- gguf-org/medgemma-1.5-4b-it-ggufModel
- Laiyf/MieDB-100kDataset
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