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Bibliografías temáticas / Histopathologie digitale

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Artículos de revistas

Literatura académica sobre el tema "Histopathologie digitale"

Autor: Grafiati

Publicado: 23 de noviembre de 2024

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Artículos de revistas sobre el tema "Histopathologie digitale"

1

Braun, Stephan A., and Doris Helbig. "Infantile digitale Fibromatose: ein seltener myofibrozytärer Tumor mit charakteristischer Histopathologie." JDDG: Journal der Deutschen Dermatologischen Gesellschaft 12, no. 12 (2014): 1141–42. http://dx.doi.org/10.1111/ddg.12450_suppl.

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2

Cummins, Donna M., Iskander H. Chaudhry, and Matthew Harries. "Scarring Alopecias: Pathology and an Update on Digital Developments." Biomedicines 9, no. 12 (2021): 1755. http://dx.doi.org/10.3390/biomedicines9121755.

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Primary cicatricial alopecias (PCA) represent a challenging group of disorders that result in irreversible hair loss from the destruction and fibrosis of hair follicles. Scalp skin biopsies are considered essential in investigating these conditions. Unfortunately, the recognised complexity of histopathologic interpretation is compounded by inadequate sampling and inappropriate laboratory processing. By sharing our successes in developing the communication pathway between the clinician, laboratory and histopathologist, we hope to mitigate some of the difficulties that can arise in managing thes

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3

Tawfeeq, Furat Nidhal, Nada A. S. Alwan, and Basim M. Khashman. "Optimization of Digital Histopathology Image Quality." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 2 (2018): 71. http://dx.doi.org/10.11591/ijai.v7.i2.pp71-77.

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<span lang="EN-US">One of the biomedical image problems is the appearance of the bubbles in the slide that could occur when air passes through the slide during the preparation process. These bubbles may complicate the process of analysing the histopathological images. The objective of this study is to remove the bubble noise from the histopathology images, and then predict the tissues that underlie it using the fuzzy controller in cases of remote pathological diagnosis. Fuzzy logic uses the linguistic definition to recognize the relationship between the input and the activity, rather tha

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4

Amgad Mohamed Khater, Nesma. "Review on Advancements in Histopathology Education through Virtual Labs, Digital Microscopy and AI." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 807–8. http://dx.doi.org/10.21275/sr241113034953.

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5

Min, Eunjung, Nurbolat Aimakov, Sangjin Lee, et al. "Multi-contrast digital histopathology of mouse organs using quantitative phase imaging and virtual staining." Biomedical Optics Express 14, no. 5 (2023): 2068. http://dx.doi.org/10.1364/boe.484516.

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Quantitative phase imaging (QPI) has emerged as a new digital histopathologic tool as it provides structural information of conventional slide without staining process. It is also capable of imaging biological tissue sections with sub-nanometer sensitivity and classifying them using light scattering properties. Here we extend its capability further by using optical scattering properties as imaging contrast in a wide-field QPI. In our first step towards validation, QPI images of 10 major organs of a wild-type mouse have been obtained followed by H&E-stained images of the corresponding tissu

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6

Ciga, Ozan, Tony Xu, and Anne Louise Martel. "Self supervised contrastive learning for digital histopathology." Machine Learning with Applications 7 (March 2022): 100198. http://dx.doi.org/10.1016/j.mlwa.2021.100198.

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7

Amrania, Hemmel, Giuseppe Antonacci, Che-Hung Chan, et al. "Digistain: a digital staining instrument for histopathology." Optics Express 20, no. 7 (2012): 7290. http://dx.doi.org/10.1364/oe.20.007290.

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8

Huss, Ralf, and Sarah E. Coupland. "Software‐assisted decision support in digital histopathology." Journal of Pathology 250, no. 5 (2020): 685–92. http://dx.doi.org/10.1002/path.5388.

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9

Martines, Roosecelis B., Jana M. Ritter, Joy Gary, et al. "Pathology and Telepathology Methods in the Child Health and Mortality Prevention Surveillance Network." Clinical Infectious Diseases 69, Supplement_4 (2019): S322—S332. http://dx.doi.org/10.1093/cid/ciz579.

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Abstract This manuscript describes the Child Health and Mortality Prevention Surveillance (CHAMPS) network approach to pathologic evaluation of minimally invasive tissue sampling (MITS) specimens, including guidelines for histopathologic examination and further diagnostics with special stains, immunohistochemistry, and molecular testing, as performed at the CHAMPS Central Pathology Laboratory (CPL) at the Centers for Disease Control and Prevention, as well as techniques for virtual discussion of these cases (telepathology) with CHAMPS surveillance locations. Based on review of MITS from the ea

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10

Mungenast, Felicitas, Achala Fernando, Robert Nica, et al. "Next-Generation Digital Histopathology of the Tumor Microenvironment." Genes 12, no. 4 (2021): 538. http://dx.doi.org/10.3390/genes12040538.

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Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular phenotypes resulting from somatic mutations and post-translational modifications. In view of a large number of genes, multiplied by differential splicing as well as post-translational protein modifications, the ability to identify and quantify the actual phenotypes of individual cell populations in situ, i.e., in their tissue environment, has become a prerequisite for understanding tumorigenesis and cancer progression. The need for qu

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