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.
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