Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in...

Area of Science:

• Artificial Intelligence
• Microscopy
• Deep Learning

Background:

• Lightweight deep learning models are crucial for efficient AI in science and medicine, reducing memory and computation.
• GPU usage in AI training and inference contributes to carbon emissions, necessitating greener alternatives.
• High-performance lightweight models offer an environmentally friendly solution.

Purpose of the Study:

• To develop and evaluate lightweight CycleGAN models for high-fidelity image modality transfer.
• To compare a fixed-channel U-Net generator against Pix2Pix and standard CycleGAN baselines.
• To explore the use of generative adversarial networks (GANs) for assessing experimental and labeling quality in fluorescence microscopy.

Main Methods:

• Utilized CycleGAN with a fixed-channel lightweight U-Net generator for modality transfer (confocal to STED images).
• Systematically compared the lightweight CycleGAN against Pix2Pix and standard CycleGAN.
• Investigated the impact of reduced model complexity (41.8M to ~9k parameters) on performance.

Main Results:

• Lightweight CycleGAN models achieved high-fidelity modality transfer despite reduced complexity.
• The fixed-channel U-Net generator demonstrated comparable or improved performance over baselines.
• GANs trained on high-quality data can serve as qualitative markers for experimental issues like artifacts or poor labeling.

Conclusions:

• Lightweight CycleGANs are effective for efficient and environmentally friendly image modality transfer in microscopy.
• The fixed-channel U-Net strategy significantly reduces parameters without compromising performance.
• GAN-based analysis offers a novel approach for quality control in fluorescence microscopy workflows.