# Digital Pathology

{% embed url="<https://www.youtube.com/watch?v=mD3FkPwp2Y4&t=2312s>" %}

#### [5 Ways to Make Histopathology Image Models More Robust to Domain Shifts: Exploring a variety of approaches: stain normalization, color augmentation, adversarial domain adaptation, model adaptation, and finetuning](https://pixelscientia.com/article-5-ways-to-make-histopathology-image-models-more-robust-to-domain-shifts.html)

{% embed url="<https://pixelscientia.com/article-5-ways-to-make-histopathology-image-models-more-robust-to-domain-shifts.html>" %}

#### **Digital Pathology & AI in routine clinical practice:From quality control to primary cancer diagnosis**

[**https://www.youtube.com/watch?v=7pRmonus9iM**](https://www.youtube.com/watch?v=7pRmonus9iM)

#### [Microvisioneer](http://www.microvisioneer.com/)

{% embed url="<http://www.microvisioneer.com/>" %}

{% embed url="<https://www.facebook.com/serdarbalcimd/posts/10154699447595679>" %}

#### [Argenit](https://argenit.com.tr/)

{% embed url="<https://argenit.com.tr/>" %}

{% embed url="<https://twitter.com/argenitt/status/814824189345624064>" %}

#### [X-WOW](https://www.x-wow.com/product-page/wsi)

{% embed url="<https://www.x-wow.com/product-page/wsi>" %}

###

#### QuPath

Mac OS için`.svs`dosyalarını açacak program ararken bulmuştum [QuPath](https://qupath.github.io/)'ı.

QuPath'ın validasyonu kolon tümörlerinde CD3, CD8, p53 ve PD-L1 skorlaması yanısıra H\&E preparatlarındaki tümör-stroma oranı ile yapılmış. Örnekler TMA üzerinde, kod ile ve otomatik analiz ile skorlanmış. Önemli bir nokta skorlamalar sağ kalım ile de karşılaştırılmış.

> [QuPath: Open source software for digital pathology image analysis](https://www.nature.com/articles/s41598-017-17204-5)\
> [Scientific Reports volume 7, Article number: 16878 (2017)](https://www.nature.com/articles/s41598-017-17204-5)\
> [doi:10.1038/s41598-017-17204-5](https://www.nature.com/articles/s41598-017-17204-5)

QuPath'ın oldukça iyi eğitim dökümanları var:

<https://github.com/qupath/qupath/wiki>

Makaledeki videolar şunlar:

[MOESM2\_ESM.mov](https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-017-17204-5/MediaObjects/41598_2017_17204_MOESM2_ESM.mov)

[MOESM3\_ESM.mov](https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-017-17204-5/MediaObjects/41598_2017_17204_MOESM3_ESM.mov)

Makalenin referans verdiği diğer dijital analiz araçlarının makaleleri ise şunlar:

* [NIH Image to ImageJ: 25 years of image analysis](https://www.nature.com/articles/nmeth.2089)
* [Fiji: an open-source platform for biological-image analysis](https://www.nature.com/articles/nmeth.2019)
* [Icy: an open bioimage informatics platform for extended reproducible research](https://www.nature.com/articles/nmeth.2075)
* [CellProfiler™: free, versatile software for automated biological image analysis](https://www.biotechniques.com/biotechniques/BiotechniquesJournal/2007/January/CellProfiler-free-versatile-software-for-automated-biological-image-analysis/biotechniques-40380.html)
* [OpenSlide: A vendor-neutral software foundation for digital pathology](http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=1;spage=27;epage=27;aulast=Goode)
* [SlideToolkit: An Assistive Toolset for the Histological Quantification of Whole Slide Images](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0110289)
* [Collaborative analysis of multi-gigapixel imaging data using Cytomine](https://academic.oup.com/bioinformatics/article/32/9/1395/1744553)
* [Immunoratio](http://jvsmicroscope.uta.fi/immunoratio/) 'yu rutin tanılarda ben kullanıyordum. Elbette tanı için direk oradaki çıkan sayıyı vermiyordum ama kendimi eğitmek için iyi bir araçtı. [Immunomembrane](http://jvsmicroscope.uta.fi/immunomembrane/) 'den ise pek iyi sonuç alamamıştım.
* [ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67](https://breast-cancer-research.biomedcentral.com/articles/10.1186/bcr2615)

<https://digitalpathologyassociation.org/whole-slide-imaging-repository>

<https://digitalpathologyassociation.org/>

* Websites

<https://pathpresenter.com/>

<http://www.rosaicollection.org/>

<http://cancer.digitalslidearchive.net/>

Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data

<https://academic.oup.com/jamia/article/20/6/1091/703483>

* Johns Hopkins Pathology Unknowns

<https://digital.pathology.johnshopkins.edu/repos/451>

* Softwares

<http://www.virasoft.com.tr/>

<http://en.mydigipath.com/>

<https://qupath.github.io/>

<http://www.microvisioneer.com/>

<http://virtualvizyon.com/>

<https://easyzoom.com/>

* Easy Scanner

<https://twitter.com/argenitt/status/814824189345624064>

* Smartphone Whole Slide Imaging(sWSI) Demo--Low Cost Slide Scanner.

<https://www.youtube.com/watch?v=orcYKy19mjQ>

<https://micro-manager.org/>

* Google's interest in digital pathology is very important for our field. See 55th minute

<https://youtu.be/Y2VF8tmLFHw?t=55m11s>

* [Assisting Pathologists in Detecting Cancer with Deep Learning](https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html)

<https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html>

* Detecting Cancer Metastases on Gigapixel Pathology Images

<https://arxiv.org/abs/1703.02442>

* Manual Microscope Image Stitching

<https://www.meyerinst.com/digital/image-stitching/>

* Motic EASY SCAN PRO Pathology Slide Scanner

<https://www.meyerinst.com/motic-easy-scan-pro-digital-slide-scanner/>

<https://www.viewsiq.com/panoptiq>

* Metadata matters: access to image data in the real world

<http://jcb.rupress.org/content/189/5/777>

* Detecting cancer in real-time with machine learning

<https://www.youtube.com/watch?v=9Mz84cwVmS0>

[An Augmented Reality Microscope for Cancer Detection](https://research.googleblog.com/2018/04/an-augmented-reality-microscope.html)

[An Augmented Reality Microscope for Real-time Automated Detection of Cancer](https://drive.google.com/open?id=1WRBCqJItaGly-9PDSMlwQ5Ldhc8lB0lf)

* Whole Slide Imaging Versus Microscopy for Primary Diagnosis in Surgical Pathology: A Multicenter Blinded Randomized Noninferiority Study of 1992 Cases (Pivotal Study).

<http://journals.lww.com/ajsp/Abstract/publishahead/Whole_Slide_Imaging_Versus_Microscopy_for_Primary.97978.aspx>

* The Gold Standard Paradox in Digital Image Analysis Manual Versus Automated Scoring as Ground Truth

Arch Pathol Lab Med. 2017 May 30. PMID: 28557614 DOI: [10.5858/arpa.2016-0386-RA](https://doi.org/10.5858/arpa.2016-0386-RA)

### About the Usage of Digital Pathology

* Whole Slide Imaging Versus Microscopy for Primary Diagnosis in Surgical Pathology: A Multicenter Blinded Randomized Noninferiority Study of 1992 Cases (Pivotal Study).

<http://journals.lww.com/ajsp/Abstract/publishahead/Whole_Slide_Imaging_Versus_Microscopy_for_Primary.97978.aspx>

* The Gold Standard Paradox in Digital Image Analysis Manual Versus Automated Scoring as Ground Truth

Arch Pathol Lab Med. 2017 May 30. PMID: 28557614 DOI: [10.5858/arpa.2016-0386-RA](https://doi.org/10.5858/arpa.2016-0386-RA)

* Whole Slide Imaging Versus Microscopy for Primary Diagnosis in Surgical Pathology: A Multicenter Blinded Randomized Noninferiority Study of 1992 Cases (Pivotal Study).

<http://journals.lww.com/ajsp/Abstract/publishahead/Whole_Slide_Imaging_Versus_Microscopy_for_Primary.97978.aspx>

* The Gold Standard Paradox in Digital Image Analysis Manual Versus Automated Scoring as Ground Truth

Arch Pathol Lab Med. 2017 May 30. PMID: 28557614 DOI: [10.5858/arpa.2016-0386-RA](https://doi.org/10.5858/arpa.2016-0386-RA)

* Whole Slide Imaging Versus Microscopy for Primary Diagnosis in Surgical Pathology: A Multicenter Blinded Randomized Noninferiority Study of 1992 Cases (Pivotal Study).

<http://journals.lww.com/ajsp/Abstract/publishahead/Whole_Slide_Imaging_Versus_Microscopy_for_Primary.97978.aspx>

* The Gold Standard Paradox in Digital Image Analysis Manual Versus Automated Scoring as Ground Truth

Arch Pathol Lab Med. 2017 May 30. PMID: 28557614 DOI: [10.5858/arpa.2016-0386-RA](https://doi.org/10.5858/arpa.2016-0386-RA)

## Digital Pathology

Mac OS için`.svs`dosyalarını açacak program ararken bulmuştum [QuPath](https://qupath.github.io/)'ı.

QuPath'ın validasyonu kolon tümörlerinde CD3, CD8, p53 ve PD-L1 skorlaması yanısıra H\&E preparatlarındaki tümör-stroma oranı ile yapılmış. Örnekler TMA üzerinde, kod ile ve otomatik analiz ile skorlanmış. Önemli bir nokta skorlamalar sağ kalım ile de karşılaştırılmış.

> [QuPath: Open source software for digital pathology image analysis](https://www.nature.com/articles/s41598-017-17204-5)\
> [Scientific Reports volume 7, Article number: 16878 (2017)](https://www.nature.com/articles/s41598-017-17204-5)\
> [doi:10.1038/s41598-017-17204-5](https://www.nature.com/articles/s41598-017-17204-5)

QuPath'ın oldukça iyi eğitim dökümanları var:

<https://github.com/qupath/qupath/wiki>

Makaledeki videolar şunlar:

[MOESM2\_ESM.mov](https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-017-17204-5/MediaObjects/41598_2017_17204_MOESM2_ESM.mov)

[MOESM3\_ESM.mov](https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-017-17204-5/MediaObjects/41598_2017_17204_MOESM3_ESM.mov)

Makalenin referans verdiği diğer dijital analiz araçlarının makaleleri ise şunlar:

* [NIH Image to ImageJ: 25 years of image analysis](https://www.nature.com/articles/nmeth.2089)
* [Fiji: an open-source platform for biological-image analysis](https://www.nature.com/articles/nmeth.2019)
* [Icy: an open bioimage informatics platform for extended reproducible research](https://www.nature.com/articles/nmeth.2075)
* [CellProfiler™: free, versatile software for automated biological image analysis](https://www.biotechniques.com/biotechniques/BiotechniquesJournal/2007/January/CellProfiler-free-versatile-software-for-automated-biological-image-analysis/biotechniques-40380.html)
* [OpenSlide: A vendor-neutral software foundation for digital pathology](http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=1;spage=27;epage=27;aulast=Goode)
* [SlideToolkit: An Assistive Toolset for the Histological Quantification of Whole Slide Images](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0110289)
* [Collaborative analysis of multi-gigapixel imaging data using Cytomine](https://academic.oup.com/bioinformatics/article/32/9/1395/1744553)
* [Immunoratio](http://jvsmicroscope.uta.fi/immunoratio/) 'yu rutin tanılarda ben kullanıyordum. Elbette tanı için direk oradaki çıkan sayıyı vermiyordum ama kendimi eğitmek için iyi bir araçtı. [Immunomembrane](http://jvsmicroscope.uta.fi/immunomembrane/) 'den ise pek iyi sonuç alamamıştım.
* [ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67](https://breast-cancer-research.biomedcentral.com/articles/10.1186/bcr2615)

<https://digitalpathologyassociation.org/whole-slide-imaging-repository>

<https://digitalpathologyassociation.org/>

* Websites

<https://pathpresenter.com/>

<http://www.rosaicollection.org/>

<http://cancer.digitalslidearchive.net/>

Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data

<https://academic.oup.com/jamia/article/20/6/1091/703483>

* Johns Hopkins Pathology Unknowns

<https://digital.pathology.johnshopkins.edu/repos/451>

* Softwares

<http://www.virasoft.com.tr/>

<http://en.mydigipath.com/>

<https://qupath.github.io/>

<http://www.microvisioneer.com/>

<http://virtualvizyon.com/>

<https://easyzoom.com/>

* Easy Scanner

<https://twitter.com/argenitt/status/814824189345624064>

* Smartphone Whole Slide Imaging(sWSI) Demo--Low Cost Slide Scanner.

<https://www.youtube.com/watch?v=orcYKy19mjQ>

<https://micro-manager.org/>

* Google's interest in digital pathology is very important for our field. See 55th minute

<https://youtu.be/Y2VF8tmLFHw?t=55m11s>

* [Assisting Pathologists in Detecting Cancer with Deep Learning](https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html)

<https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html>

* Detecting Cancer Metastases on Gigapixel Pathology Images

<https://arxiv.org/abs/1703.02442>

* Manual Microscope Image Stitching

<https://www.meyerinst.com/digital/image-stitching/>

* Motic EASY SCAN PRO Pathology Slide Scanner

<https://www.meyerinst.com/motic-easy-scan-pro-digital-slide-scanner/>

<https://www.viewsiq.com/panoptiq>

* Metadata matters: access to image data in the real world

<http://jcb.rupress.org/content/189/5/777>

* Detecting cancer in real-time with machine learning

<https://www.youtube.com/watch?v=9Mz84cwVmS0>

[An Augmented Reality Microscope for Cancer Detection](https://research.googleblog.com/2018/04/an-augmented-reality-microscope.html)

[An Augmented Reality Microscope for Real-time Automated Detection of Cancer](https://drive.google.com/open?id=1WRBCqJItaGly-9PDSMlwQ5Ldhc8lB0lf)

Mac OS için`.svs`dosyalarını açacak program ararken bulmuştum [QuPath](https://qupath.github.io/)'ı.

QuPath'ın validasyonu kolon tümörlerinde CD3, CD8, p53 ve PD-L1 skorlaması yanısıra H\&E preparatlarındaki tümör-stroma oranı ile yapılmış. Örnekler TMA üzerinde, kod ile ve otomatik analiz ile skorlanmış. Önemli bir nokta skorlamalar sağ kalım ile de karşılaştırılmış.

> [QuPath: Open source software for digital pathology image analysis](https://www.nature.com/articles/s41598-017-17204-5)\
> [Scientific Reports volume 7, Article number: 16878 (2017)](https://www.nature.com/articles/s41598-017-17204-5)\
> [doi:10.1038/s41598-017-17204-5](https://www.nature.com/articles/s41598-017-17204-5)

QuPath'ın oldukça iyi eğitim dökümanları var:

<https://github.com/qupath/qupath/wiki>

Makaledeki videolar şunlar:

[MOESM2\_ESM.mov](https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-017-17204-5/MediaObjects/41598_2017_17204_MOESM2_ESM.mov)

[MOESM3\_ESM.mov](https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-017-17204-5/MediaObjects/41598_2017_17204_MOESM3_ESM.mov)

Makalenin referans verdiği diğer dijital analiz araçlarının makaleleri ise şunlar:

* [NIH Image to ImageJ: 25 years of image analysis](https://www.nature.com/articles/nmeth.2089)
* [Fiji: an open-source platform for biological-image analysis](https://www.nature.com/articles/nmeth.2019)
* [Icy: an open bioimage informatics platform for extended reproducible research](https://www.nature.com/articles/nmeth.2075)
* [CellProfiler™: free, versatile software for automated biological image analysis](https://www.biotechniques.com/biotechniques/BiotechniquesJournal/2007/January/CellProfiler-free-versatile-software-for-automated-biological-image-analysis/biotechniques-40380.html)
* [OpenSlide: A vendor-neutral software foundation for digital pathology](http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=1;spage=27;epage=27;aulast=Goode)
* [SlideToolkit: An Assistive Toolset for the Histological Quantification of Whole Slide Images](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0110289)
* [Collaborative analysis of multi-gigapixel imaging data using Cytomine](https://academic.oup.com/bioinformatics/article/32/9/1395/1744553)
* [Immunoratio](http://jvsmicroscope.uta.fi/immunoratio/) 'yu rutin tanılarda ben kullanıyordum. Elbette tanı için direk oradaki çıkan sayıyı vermiyordum ama kendimi eğitmek için iyi bir araçtı. [Immunomembrane](http://jvsmicroscope.uta.fi/immunomembrane/) 'den ise pek iyi sonuç alamamıştım.
* [ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67](https://breast-cancer-research.biomedcentral.com/articles/10.1186/bcr2615)

<https://digitalpathologyassociation.org/whole-slide-imaging-repository>

<https://digitalpathologyassociation.org/>

* Websites

<https://pathpresenter.com/>

<http://www.rosaicollection.org/>

<http://cancer.digitalslidearchive.net/>

Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data

<https://academic.oup.com/jamia/article/20/6/1091/703483>

* Johns Hopkins Pathology Unknowns

<https://digital.pathology.johnshopkins.edu/repos/451>

* Softwares

<http://www.virasoft.com.tr/>

<http://en.mydigipath.com/>

<https://qupath.github.io/>

<http://www.microvisioneer.com/>

<http://virtualvizyon.com/>

<https://easyzoom.com/>

* Easy Scanner

<https://twitter.com/argenitt/status/814824189345624064>

* Smartphone Whole Slide Imaging(sWSI) Demo--Low Cost Slide Scanner.

<https://www.youtube.com/watch?v=orcYKy19mjQ>

<https://micro-manager.org/>

* Google's interest in digital pathology is very important for our field. See 55th minute

<https://youtu.be/Y2VF8tmLFHw?t=55m11s>

* [Assisting Pathologists in Detecting Cancer with Deep Learning](https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html)

<https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html>

* Detecting Cancer Metastases on Gigapixel Pathology Images

<https://arxiv.org/abs/1703.02442>

* Manual Microscope Image Stitching

<https://www.meyerinst.com/digital/image-stitching/>

* Motic EASY SCAN PRO Pathology Slide Scanner

<https://www.meyerinst.com/motic-easy-scan-pro-digital-slide-scanner/>

<https://www.viewsiq.com/panoptiq>

* Metadata matters: access to image data in the real world

<http://jcb.rupress.org/content/189/5/777>

* Detecting cancer in real-time with machine learning

<https://www.youtube.com/watch?v=9Mz84cwVmS0>

[An Augmented Reality Microscope for Cancer Detection](https://research.googleblog.com/2018/04/an-augmented-reality-microscope.html)

[An Augmented Reality Microscope for Real-time Automated Detection of Cancer](https://drive.google.com/open?id=1WRBCqJItaGly-9PDSMlwQ5Ldhc8lB0lf)

***

* Single slide scanner

[optiscan](https://www.blabmarket.com/urun/optika-optiscan-dijital-mikroskop-tarayici)

***

{% embed url="<https://www.youtube.com/watch?v=zNRxF-TqUnI>" %}


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