CycleGAN network for photoacoustic histology

An unsupervised deep learning algorithm based on cycle-consistent generative adversarial networks (CycleGAN ) converted the UV-PAM images into H&E-like pseudocolor histologic images, allowing the pathologists to readily identify the cancerous features following existing pattern-recognition parameters. Unlike supervised deep learning methods such as generational adversarial networks (GAN), the unsupervised deep learning method based on CycleGAN does not require coupled pairs of stained and unstained images. It avoids the need for well-aligned UV-PAM and H&E-stained images for neural network training, which can be challenging to acquire due to artifacts caused by sample preparation-induced morphology changes.