YeaZ - Revision history

Nash at 18:38, 13 November 2020

2020-11-13T18:38:51Z

Nash at 18:38, 13 November 2020

2020-11-13T18:38:22Z

Nash at 18:37, 13 November 2020

2020-11-13T18:37:53Z

Nash at 18:37, 13 November 2020

2020-11-13T18:37:45Z

Nash at 18:36, 13 November 2020

2020-11-13T18:36:55Z

Nash at 18:36, 13 November 2020

2020-11-13T18:36:45Z

Nash at 18:35, 13 November 2020

2020-11-13T18:35:15Z

Nash: Created page with "https://www.epfl.ch/labs/lpbs/data-and-software/|YeaZ] is a system for efficiently and accurately segmenting microscopy images of yeast cells. It contains a convolutional neur..."

2020-11-13T18:34:51Z

Created page with "https://www.epfl.ch/labs/lpbs/data-and-software/|YeaZ] is a system for efficiently and accurately segmenting microscopy images of yeast cells. It contains a convolutional neur..."

New page

https://www.epfl.ch/labs/lpbs/data-and-software/|YeaZ] is a system for efficiently and accurately segmenting microscopy images of yeast cells. It contains a convolutional neural network, with an underlying training set of high-quality segmented yeast images, as well as a graphical user interface and a web application to employ, test, and expand the system.

[[sgdwiki-yeaz-screenshot.png]]

The system contains a Python based application with graphical user interface available on GitHub (https://github.com/lpbsscientist/YeaZ-GUI), as well as standalone apps for both Windows and Mac based computers, as well as training sets. Additional information is available in the accompanying Nature Communications paper by [https://www.nature.com/articles/s41467-020-19557-4|Dietler et al., 2020](https://www.nature.com/articles/s41467-020-19557-4).

YeaZ was created at École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
Please contact [https://www.epfl.ch/labs/lpbs/professor-rahi/ Sahand Jamal Rahi] with questions, or ideas for improvements.

← Older revision		Revision as of 18:38, 13 November 2020
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−	YeaZ was created at École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland	+	YeaZ was created at École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland. Please contact [https://www.epfl.ch/labs/lpbs/professor-rahi/ Sahand Jamal Rahi] with questions, or ideas for improvements.
−	Please contact [https://www.epfl.ch/labs/lpbs/professor-rahi/ Sahand Jamal Rahi] with questions, or ideas for improvements.

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 [[Image:sgdwiki-yeaz-screenshot.png]]
-The system contains a Python based application with graphical user interface available on [https://github.com/lpbsscientist/YeaZ-GUI|GitHub], as well as standalone apps for both Windows and Mac based computers, and training sets. Additional information is available in the accompanying Nature Communications paper by [https://www.nature.com/articles/s41467-020-19557-4|Dietler et al., 2020]
+The system contains a Python based application with graphical user interface available on [https://github.com/lpbsscientist/YeaZ-GUI GitHub], as well as standalone apps for both Windows and Mac based computers, and training sets. Additional information is available in the accompanying Nature Communications paper by [https://www.nature.com/articles/s41467-020-19557-4 Dietler et al., 2020]
 YeaZ was created at École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
 Please contact [https://www.epfl.ch/labs/lpbs/professor-rahi/ Sahand Jamal Rahi] with questions, or ideas for improvements.

← Older revision		Revision as of 18:37, 13 November 2020
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−	[https://www.epfl.ch/labs/lpbs/data-and-software/ \|YeaZ] is a system for efficiently and accurately segmenting microscopy images of yeast cells. It contains a convolutional neural network, with an underlying training set of high-qual\|ty segmented yeast images, as well as a graphical user interface and a web application to employ, test, and expand the system.	+	[https://www.epfl.ch/labs/lpbs/data-and-software/ YeaZ] is a system for efficiently and accurately segmenting microscopy images of yeast cells. It contains a convolutional neural network, with an underlying training set of high-qual\|ty segmented yeast images, as well as a graphical user interface and a web application to employ, test, and expand the system.

	[[Image:sgdwiki-yeaz-screenshot.png]]		[[Image:sgdwiki-yeaz-screenshot.png]]

← Older revision		Revision as of 18:37, 13 November 2020
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−	[https://www.epfl.ch/labs/lpbs/data-and-software/\|YeaZ] is a system for efficiently and accurately segmenting microscopy images of yeast cells. It contains a convolutional neural network, with an underlying training set of high-qual\|ty segmented yeast images, as well as a graphical user interface and a web application to employ, test, and expand the system.	+	[https://www.epfl.ch/labs/lpbs/data-and-software/ \|YeaZ] is a system for efficiently and accurately segmenting microscopy images of yeast cells. It contains a convolutional neural network, with an underlying training set of high-qual\|ty segmented yeast images, as well as a graphical user interface and a web application to employ, test, and expand the system.

	[[Image:sgdwiki-yeaz-screenshot.png]]		[[Image:sgdwiki-yeaz-screenshot.png]]

← Older revision		Revision as of 18:36, 13 November 2020
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−	[https://www.epfl.ch/labs/lpbs/data-and-software/\|YeaZ] is a system for efficiently and accurately segmenting microscopy images of yeast cells. It contains a convolutional neural network, with an underlying training set of high-~~quality~~ segmented yeast images, as well as a graphical user interface and a web application to employ, test, and expand the system.	+	[https://www.epfl.ch/labs/lpbs/data-and-software/\|YeaZ] is a system for efficiently and accurately segmenting microscopy images of yeast cells. It contains a convolutional neural network, with an underlying training set of high-qual\|ty segmented yeast images, as well as a graphical user interface and a web application to employ, test, and expand the system.

	[[Image:sgdwiki-yeaz-screenshot.png]]		[[Image:sgdwiki-yeaz-screenshot.png]]

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 https://www.epfl.ch/labs/lpbs/data-and-software/|YeaZ] is a system for efficiently and accurately segmenting microscopy images of yeast cells. It contains a convolutional neural network, with an underlying training set of high-quality segmented yeast images, as well as a graphical user interface and a web application to employ, test, and expand the system.
-[[sgdwiki-yeaz-screenshot.png]]
+[[Image:sgdwiki-yeaz-screenshot.png]]
 The system contains a Python based application with graphical user interface available on GitHub (https://github.com/lpbsscientist/YeaZ-GUI), as well as standalone apps for both Windows and Mac based computers, as well as training sets. Additional information is available in the accompanying Nature Communications paper by [https://www.nature.com/articles/s41467-020-19557-4|Dietler et al., 2020](https://www.nature.com/articles/s41467-020-19557-4).