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Medical image local augmentation via text- and mask-guided diffusion model.

Pei Cao¹, Donghao Li¹, Xinlu Li¹

¹School of Artificial Intelligence and Big Data, Hefei University, Hefei, Anhui, China.

Medical Physics

|June 25, 2026

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This study introduces a novel text- and mask-guided local augmentation method for medical images, enhancing diversity in AI analysis. The technique effectively modifies local image regions while preserving global consistency, advancing intelligent medical imaging.

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Computer Vision

Background:

Medical image scarcity hinders intelligent analysis and precision medicine.
Current data augmentation methods lack control over local image details.
Advanced techniques are needed to overcome data limitations in medical AI.

Purpose of the Study:

To propose a text- and mask-guided local augmentation method for medical images.
To enhance the diversity and quality of synthesized medical images.
To enable fine-grained control over local image regions for data augmentation.

Main Methods:

Utilized a pre-trained MedSAM model for precise segmentation of target regions, generating masks.
Developed semantically relevant and task-specific text prompts for diverse medical imaging data.

Keywords:

diffusion model local augmentation medical images

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Published on: November 30, 2022

Integrated masks and text prompts into a diffusion generative model for controlled local perturbations.

Main Results:

Achieved significant reduction in local structural similarity (97.9% on chest x-ray, 103.3% on pelvic CT, 42.2% on brain CT) within masked regions.
Demonstrated effective modulation of structural features in local regions.
Maintained global texture consistency in generated synthetic medical images.

Conclusions:

Presents a new pathway for controlled data augmentation in medical imaging.
Facilitates advancements in intelligent medical image analysis.
Lays the groundwork for future research in fine-grained medical image generation.