Lightweight CycleGAN Models for Cross-Modality Image Transformation and Experimental Quality Assessment in Fluorescence Microscopy
This work addresses computational efficiency and experimental validation for researchers in fluorescence microscopy, though it is incremental as it builds on existing CycleGAN methods.
The authors tackled the problem of high computational cost in cross-modality image transformation for fluorescence microscopy by developing a lightweight CycleGAN that reduces trainable parameters from 41.8 million to about nine thousand, achieving faster training and lower memory usage while maintaining performance. They also introduced the GAN as a diagnostic tool to detect issues like photobleaching or artifacts by comparing generated outputs with experimental images.
Lightweight deep learning models offer substantial reductions in computational cost and environmental impact, making them crucial for scientific applications. We present a lightweight CycleGAN for modality transfer in fluorescence microscopy (confocal to super-resolution STED/deconvolved STED), addressing the common challenge of unpaired datasets. By replacing the traditional channel-doubling strategy in the U-Net-based generator with a fixed channel approach, we drastically reduce trainable parameters from 41.8 million to approximately nine thousand, achieving superior performance with faster training and lower memory usage. We also introduce the GAN as a diagnostic tool for experimental and labeling quality. When trained on high-quality images, the GAN learns the characteristics of optimal imaging; deviations between its generated outputs and new experimental images can reveal issues such as photobleaching, artifacts, or inaccurate labeling. This establishes the model as a practical tool for validating experimental accuracy and image fidelity in microscopy workflows.