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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Lymph_node_with_metastatic_melanoma_-_by_Gabriel_Caponetti,_MD.jpg|200px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''[[Anatomical pathology]]''' (or '''Anatomic pathology''') is a medical specialty that is concerned with the gross, microscopic, chemical, immunologic, and molecular examination of organs, tissues, and whole bodies (as in autopsy) to determine the presence of disease. Italian scientist Giovanni Battista Morgagni is widely considered the founding father of anatomic pathology.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Anatomical pathology is one of two branches of pathology, the other being [[clinical pathology]], the diagnosis of disease through the laboratory analysis of bodily fluids. Often, pathologists practice both anatomical and clinical pathology, a combination known as general pathology. The distinction between anatomic and clinical pathology is increasingly blurred by the introduction of technologies that require new expertise and the need to provide patients and referring physicians with integrated diagnostic reports.
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
 
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The procedures used in anatomic pathology include gross examination, [[histopathology]], immunohistochemistry, ''in situ'' hybridization, [[cytopathology]], electron microscopy, tissue cytogenetics, and flow immunophenotyping. Anatomic pathology differs from clinical pathology in several ways, often led by the differentiation in [[laboratory]] workflow. ('''[[Anatomical pathology|Full article...]]''')<br />
{{flowlist |
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* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
''Recently featured'': [[Information]], [[Clinical laboratory]], [[Hospital information system]]
* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
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* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
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Revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
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