MieDB-100k: A Comprehensive Dataset for Medical Image Editing
This addresses data scarcity for researchers and practitioners in medical image editing, though it is incremental as it focuses on dataset creation rather than a new method.
The authors tackled the problem of limited data for medical image editing by introducing MieDB-100k, a large-scale dataset, and showed that models trained on it consistently outperform existing models with strong generalization.
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-based data synthetic methods, followed by rigorous manual inspection to ensure clinical fidelity. Extensive experiments demonstrate that model trained with MieDB-100k consistently outperform both open-source and proprietary models while exhibiting strong generalization ability. We anticipate that this dataset will serve as a cornerstone for future advancements in specialized medical image editing.