MieDB-100k: A Comprehensive Dataset for Medical Image Editing

TL;DR

MieDB-100k is a large, diverse, high-quality dataset designed to advance text-guided medical image editing by addressing current data limitations.

Contribution

The paper introduces MieDB-100k, a comprehensive dataset constructed through expert models and synthetic methods, enabling improved medical image editing models.

Findings

Models trained on MieDB-100k outperform existing models.

The dataset demonstrates strong generalization in medical image editing.

Rigorous manual inspection ensures clinical fidelity.

Abstract

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.