Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Fraggetta Diagnostics21 11-11.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:Best practice recommendations for the implementation of a digital pathology workflow in the anatomic pathology laboratory by the European Society of Digital and Integrative Pathology (ESDIP)|Best practice recommendations for the implementation of a digital pathology workflow in the anatomic pathology laboratory by the European Society of Digital and Integrative Pathology (ESDIP)]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The interest in implementing [[digital pathology]] (DP) [[workflow]]s to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the [[Food and Drug Administration]] (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real-world data suggest that a fully digital approach to histological workflow has been implemented in only a minority of [[Clinical laboratory|pathology laboratories]]. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation with: (a) how to identify the scope and the boundaries of the DP transformation; (b) how to introduce [[Laboratory automation|automation]] to reduce errors; (c) how to introduce appropriate [[quality control]] to guarantee the safety of the process; and (d) addressing the hardware and software needed to implement DP systems inside the pathology laboratory. ... ('''[[Journal:Best practice recommendations for the implementation of a digital pathology workflow in the anatomic pathology laboratory by the European Society of Digital and Integrative Pathology (ESDIP)|Full article...]]''')<br />
[[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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Latest 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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