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QEMCGAN: Quantized Evolutionary Gradient Aware Multiobjective Cyclic GAN for Medical Image Translation.

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Summary

This study introduces a new Quantized Evolutionary Gradient Aware Multiobjective Cyclic GAN (QEMCGAN) for medical image translation. The novel approach enhances image realism and preserves details while improving efficiency, even with reduced model size.

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Generative adversarial networks (GANs) excel in medical image translation but suffer from training instability, mode collapse, and the cost of paired data collection.
Existing cyclic GANs can translate cross-domain images but lack comprehensive solutions for diverse datasets and applications.

Purpose of the Study:

To propose a novel Quantized Evolutionary Gradient Aware Multiobjective Cyclic GAN (QEMCGAN) to address limitations in medical image-to-image translation.
To improve training stability, image diversity, and efficiency in GAN-based medical imaging.

Main Methods:

Employed evolutionary computation, multiobjective optimization, and an intelligent selection scheme, including simulated annealing and Pareto ranking.
Integrated model quantization for suitability in low-cost IoT-based applications.
Utilized three fitness criteria to mitigate local optima stagnation.

Main Results:

The proposed EMCGAN and QEMCGAN generated more visually realistic medical images compared to existing methods.
The models effectively preserved both background information and salient features in translated images.
QEMCGAN demonstrated comparable performance to baseline approaches with a halved model size, indicating improved efficiency.

Conclusions:

QEMCGAN offers a robust and efficient solution for medical image-to-image translation, overcoming common GAN challenges.
The integration of evolutionary algorithms and model quantization enhances performance and applicability, particularly for resource-constrained environments.