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- W4385624264 abstract "Histopathology refers to the observation of tissues to identify the manifestation of diseases, e.g., cancer. Tiny tissue samples are taken from the patient and studied through a microscope; the analysis of the different cells, particularly their nuclei and other structures, allows for disease detection. The biological specimens need some preparation, namely Hematoxylin and Eosin (H&E) staining is often used to highlight nuclei and cytoplasm. Although staining is fundamental, given that cells are transparent when imaged, it is still highly affected by casual errors: colors change when a small preparation step is slightly different and even when a different microscope is used. This factor leads to Computer Aided Detection (CAD) systems losing performance. Therefore, to solve this problem and allow for the integration of multiple low-dimensional datasets, we propose a CycleGAN-based architecture exploiting PatchGAN and U-Net backbones as discriminators and generators, respectively, demonstrating an improvement in mean Structural Similarity Index Measure (SSIM) over the one computed on the original datasets of around 1.8%." @default.
- W4385624264 created "2023-08-08" @default.
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- W4385624264 date "2023-07-06" @default.
- W4385624264 modified "2023-09-23" @default.
- W4385624264 title "Stain Transfer using CycleGAN for Histopathological Images" @default.
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- W4385624264 doi "https://doi.org/10.1109/eurocon56442.2023.10199027" @default.
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