Histology image sequence representation using a deep generative neural network

Abstract The use of advanced imaging technologies has enabled the generation of high‐resolution images in the medical domain. However, for efficient processing with limited computational resources, such images are generally processed as groups of small sub‐images or patches. In this study, we propose a generative neural network that, when trained using a sequence of histological images, is capable of reconstructing the sequence when required. We demonstrate that this model, trained on sub‐images of a high‐resolution image, can serve as an alternative representation of larger images because it reconstructs the original image as a sequence of sub‐images. Experimental results on images collected from the Grand Challenge breast cancer dataset demonstrate excellent PSNR, SSIM, and UQI values for the high‐resolution images reconstructed by the proposed model. When analyzing the latent space of the models for different cancer images, individual images appear to occupy specific points within the space, which are distinguishable but non‐separable, indicating that the proposed model is capable of capturing and reconstructing the structural features of individual images.