CoordConv-ControlDDPM: A Text-Guided Approach to Medical Image Augmentation for Improved Segmentation

Area of Science:

• Medical Imaging
• Machine Learning
• Computer Vision

Background:

• Limited availability of medical image data is a significant challenge for developing robust machine learning models in healthcare.
• Data scarcity hinders the training and generalization capabilities of AI algorithms for medical image analysis.

Purpose of the Study:

• To address the challenge of limited medical image data by employing generative data augmentation.
• To develop a novel method for synthesizing realistic and diverse medical images to enhance training datasets for segmentation tasks.

Main Methods:

• Proposed CoordConv-ControlDDPM, a novel approach based on ControlNet for generating augmented medical images.
• The method synthesizes new medical images from binary masks and text descriptions.
• Integrated synthesized images into existing segmentation datasets for training.

Main Results:

• Demonstrated the ability to produce controllable and diverse augmented medical image data.
• Achieved significant improvements in medical image segmentation performance.
• Showcased the effectiveness of the proposed generative approach in enriching training datasets.

Conclusions:

• The CoordConv-ControlDDPM method effectively overcomes data limitations in medical imaging.
• Generative data augmentation using this novel approach enhances the robustness and performance of machine learning models for segmentation.
• The method offers a promising solution for expanding medical image datasets and improving diagnostic accuracy.