Relevant bibliographies by topics / Histopathologie digitale

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  1. Journal articles

Academic literature on the topic 'Histopathologie digitale'

Author: Grafiati
Published: 23 November 2024

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Journal articles on the topic "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 these conditions. We provide insight from clinical and pathology practice into how diagnoses are derived and the key histological features observed across the most common PCAs seen in practice. Additionally, we highlight the opportunities that have emerged with advances in digital pathology and how these technologies may be used to develop clinicopathological relationships, improve working practices, enhance remote learning, reduce inefficiencies, optimise diagnostic yield, and harness the potential of artificial intelligence (AI).
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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 than using difficult numerical equation. Mainly there are five parts, starting with accepting the image, passing through removing the bubbles, and ending with predict the tissues. These were implemented by defining membership functions between colours range using MATLAB. Results: 50 histopathological images were tested on four types of membership functions (MF); the results show that (nine-triangular) MF get 75.4% correctly predicted pixels versus 69.1, 72.31 and 72% for (five- triangular), (five-Gaussian) and (nine-Gaussian) respectively. Conclusions: In line with the era of digitally driven e-pathology, this process is essentially recommended to ensure quality interpretation and analyses of the processed slides; thus overcoming relevant limitations.</span>
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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 tissue sections. Furthermore, we utilized deep learning model based on generative adversarial network (GAN) architecture for virtual staining of phase delay images to a H&E-equivalent brightfield (BF) image analogues. Using the structural similarity index, we demonstrate similarities between virtually stained and H&E histology images. Whereas the scattering-based maps look rather similar to QPI phase maps in the kidney, the brain images show significant improvement over QPI with clear demarcation of features across all regions. Since our technology provides not only structural information but also unique optical property maps, it could potentially become a fast and contrast-enriched histopathology technique.
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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 early phase of CHAMPS, the CPL has developed standardized histopathology-based algorithms for achieving diagnoses from MITS and telepathology procedures in conjunction with the CHAMPS sites, with the use of whole slide scanners and digital image archives, for maximizing concurrence and knowledge sharing between site and CPL pathologists. These algorithms and procedures, along with lessons learned from initial implementation of these approaches, guide pathologists at the CPL and CHAMPS sites through standardized diagnostics of MITS cases, and allow for productive, real-time case discussions and consultations.
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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 quantitative analyses has led to a renaissance of optical instruments and imaging techniques. With the emergence of precision medicine, automated analysis of a constantly increasing number of cellular markers and their measurement in spatial context have become increasingly necessary to understand the molecular mechanisms that lead to different pathways of disease progression in individual patients. In this review, we summarize the joint effort that academia and industry have undertaken to establish methods and protocols for molecular profiling and immunophenotyping of cancer tissues for next-generation digital histopathology—which is characterized by the use of whole-slide imaging (brightfield, widefield fluorescence, confocal, multispectral, and/or multiplexing technologies) combined with state-of-the-art image cytometry and advanced methods for machine and deep learning.
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