Extracting low dimensional representations from large size whole slide images using deep convolutional autoencoders

Background and objective: Artificial intelligence-based analysis of medical images has recently become a trendy field of study. The most significant effect on these systems to produce reliable and high-performance results is the amount of accessed data. Although whole-slide images (WSI), one of the current imaging techniques used in histopathology, storage costs are very expensive. Therefore, low-dimensional visuals representing of WSIs is an essential field of study. Methods: In this study, deep auto-encoder-based models are designed to create low dimensional representations for WSI. The size reduction was performed for the input images in different sizes, at the ratios of 1: 3, 1: 6, and 1: 12, respectively. Results: Similarity index measure values were obtained as high as 0.957 and 0.938, respectively, using 128 × 128 × 3 dimensional patches in the size reduction process of 1: 3 and 1:12 on test WSI. Conclusion: The proposed model has a structure that can be applied in compression of WSIs and secure data transfer. By using these representations, both storage costs and the high-level hardware costs required by deep learning algorithms can be significantly reduced.