2022
DOI: 10.3389/fphys.2022.832457
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Tissue Cytometry With Machine Learning in Kidney: From Small Specimens to Big Data

Abstract: Advances in cellular and molecular interrogation of kidney tissue have ushered a new era of understanding the pathogenesis of kidney disease and potentially identifying molecular targets for therapeutic intervention. Classifying cells in situ and identifying subtypes and states induced by injury is a foundational task in this context. High resolution Imaging-based approaches such as large-scale fluorescence 3D imaging offer significant advantages because they allow preservation of tissue architecture and provi… Show more

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Cited by 4 publications
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“…There are relatively few techniques 7 – 9 available to extract spatial data from stained kidney tissue that can study 10 4 cells or more per sample, as is routinely achieved with single cell approaches. More commonly, IF analyses require time-consuming scoring or counting procedures or semi-automated thresholding approaches.…”
Section: Introductionmentioning
confidence: 99%
“…There are relatively few techniques 7 – 9 available to extract spatial data from stained kidney tissue that can study 10 4 cells or more per sample, as is routinely achieved with single cell approaches. More commonly, IF analyses require time-consuming scoring or counting procedures or semi-automated thresholding approaches.…”
Section: Introductionmentioning
confidence: 99%
“…There are relatively few techniques [7][8][9] available to extract spatial data from stained kidney tissue that can study 10 4 cells or more per sample, as is routinely achieved with single cell approaches. More commonly, IF analyses require time-consuming scoring or counting procedures or semi-automated thresholding approaches.…”
Section: Introductionmentioning
confidence: 99%