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

2026-02-10

AI Summary

This work addresses the scarcity of high-quality, large-scale multimodal datasets that jointly support understanding and generation in medical image editing—a key bottleneck hindering the advancement of generative models in this domain. To bridge this gap, the study introduces the first systematic categorization of medical image editing tasks into three types: perception, modification, and transformation. Building upon this framework, the authors construct MieDB-100k, a dataset comprising 100,000 samples generated via modality-specific expert models and rule-driven synthesis, followed by rigorous human validation to ensure clinical fidelity and diversity. Models trained on MieDB-100k demonstrate significantly superior performance and generalization compared to existing open- and closed-source alternatives, establishing a robust foundation for future research in medical image editing.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• medical image editing
• data scarcity
• dataset diversity
• multimodal generative models
• clinical fidelity

Innovation

• medical image editing
• text-guided generation
• MieDB-100k
• data curation
• multimodal generative models