HistoStarGAN: A unified approach to stain normalisation, stain transfer and stain invariant segmentation in renal histopathology
نویسندگان
چکیده
Virtual stain transfer is a promising area of research in Computational Pathology, which has great potential to alleviate important limitations when applying deep-learning-based solutions such as lack annotations and sensitivity domain shift. However, the literature, majority virtual staining approaches are trained for specific or combination, their extension unseen stainings requires acquisition additional data training. In this paper, we propose HistoStarGAN, unified framework that performs between multiple stainings, normalisation invariant segmentation, all one inference model. We demonstrate generalisation abilities proposed solution perform diverse accurate segmentation over numerous first demonstration field. Moreover, pre-trained HistoStarGAN model can serve synthetic generator, paves way use fully annotated image improve training deep learning-based algorithms. To illustrate capabilities our approach, well risks microscopy domain, inspired by applications natural images, generated KidneyArtPathology, artificial dataset renal pathology.
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ژورنال
عنوان ژورنال: Knowledge Based Systems
سال: 2023
ISSN: ['1872-7409', '0950-7051']
DOI: https://doi.org/10.1016/j.knosys.2023.110780