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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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MedIENet: medical image enhancement network based on conditional latent diffusion model.

Weizhen Yuan¹, Yue Feng², Tiancai Wen^3,4

¹School of Electronic and Information Engineering, Wuyi University, Jiangmen, Guangdong, 529020, China.

BMC Medical Imaging

|September 27, 2025

Summary

Generating high-quality medical images with MedIENet, a novel conditional latent diffusion model, addresses data scarcity for deep learning. This enhances downstream classification tasks, improving diagnostic accuracy.

Keywords:

Attention mechanism Data enhancement Diffusion model Medical image generation

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Area of Science:

Medical Imaging
Artificial Intelligence
Deep Learning

Background:

Deep learning models require extensive datasets, which are challenging to acquire in medical imaging due to privacy concerns and high costs.
Existing methods struggle to generate sufficient high-fidelity medical images for training AI models.

Purpose of the Study:

To develop a novel medical image enhancement network, MedIENet, based on a conditional latent diffusion model.
To address the challenge of limited medical image data for deep learning applications.

Main Methods:

Proposed MedIENet utilizes a conditional latent diffusion model with a multi-attention module in a U-Net backbone for enhanced image generation.
Rotary Position Embedding (RoPE) and cross-attention mechanisms were integrated to capture positional information and class embeddings.

Main Results:

MedIENet demonstrated superior performance in generating diverse and high-fidelity medical images across Chest CT-Scan, Chest X-Ray, and Tongue datasets.
Downstream classification tasks using ResNet50 showed significant improvement in Area Under the Receiver Operating Characteristic curve (AUROC) when using MedIENet-generated data, with increases of 4-7%.

Conclusions:

The generated medical images from MedIENet effectively augment training datasets, significantly improving the performance of downstream classification tasks.
MedIENet offers a viable solution to the critical issue of medical image data scarcity in deep learning.