<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1257</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-02-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides</title>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2004.05838</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/1917</enrichment>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Anne</first_name>
      <last_name>Petrick</last_name>
    </author>
    <author>
      <first_name>Kristina</first_name>
      <last_name>Weiler</last_name>
    </author>
    <author>
      <first_name>Agnes C.</first_name>
      <last_name>Gläsel</last_name>
    </author>
    <author>
      <first_name>Marco</first_name>
      <last_name>Fragoso-Garcia</last_name>
    </author>
    <author>
      <first_name>Sophie</first_name>
      <last_name>Merz</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Bartenschlager</last_name>
    </author>
    <author>
      <first_name>Judith</first_name>
      <last_name>Hoppe</last_name>
    </author>
    <author>
      <first_name>Alina</first_name>
      <last_name>Langenhagen</last_name>
    </author>
    <author>
      <first_name>Anne-Katherine</first_name>
      <last_name>Jasensky</last_name>
    </author>
    <author>
      <first_name>Jörn</first_name>
      <last_name>Voigt</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
  </doc>
  <doc>
    <id>2931</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume>9</volume>
    <articleNumber>269</articleNumber>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-10-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Inter-species cell detection - datasets on pulmonary hemosiderophages in equine, human and feline specimens</title>
    <abstract language="eng">Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset, which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologist. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered.</abstract>
    <parentTitle language="eng">Scientific Data</parentTitle>
    <identifier type="issn">2052-4463</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-29319</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1038/s41597-022-01389-0</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Jenny</first_name>
      <last_name>Hill</last_name>
    </author>
    <author>
      <first_name>Jason</first_name>
      <last_name>Stayt</last_name>
    </author>
    <author>
      <first_name>Dorothee</first_name>
      <last_name>Bienzle</last_name>
    </author>
    <author>
      <first_name>Lutz</first_name>
      <last_name>Welker</last_name>
    </author>
    <author>
      <first_name>Frauke</first_name>
      <last_name>Wilm</last_name>
    </author>
    <author>
      <first_name>Jörn</first_name>
      <last_name>Voigt</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2931/s41597-022-01389-0.pdf</file>
  </doc>
  <doc>
    <id>1262</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>181</pageFirst>
    <pageLast>190</pageLast>
    <pageNumber/>
    <edition/>
    <issue>156</issue>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>PMLR</publisherName>
    <publisherPlace>[s. l.]</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Robust quad-tree based registration on whole slide images</title>
    <parentTitle language="eng">Proceedings of Machine Learning Research: Proceedings of COMPAY 2021</parentTitle>
    <identifier type="url">https://proceedings.mlr.press/v156/marzahl21a.html</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">MICCAI Workshop on Computational Pathology, online, 27.09.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Frauke</first_name>
      <last_name>Wilm</last_name>
    </author>
    <author>
      <first_name>Lars</first_name>
      <last_name>Tharun</last_name>
    </author>
    <author>
      <first_name>Sven</first_name>
      <last_name>Perner</last_name>
    </author>
    <author>
      <first_name>Christine</first_name>
      <last_name>Kröger</last_name>
    </author>
    <author>
      <first_name>Jörn</first_name>
      <last_name>Voigt</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Registration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pathology</value>
    </subject>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
  </doc>
  <doc>
    <id>1917</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>24</pageFirst>
    <pageLast>32</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-03-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides</title>
    <parentTitle language="eng">Medical Image Computing and Computer Assisted Intervention – MICCAI 2020</parentTitle>
    <identifier type="issn">1611-3349</identifier>
    <identifier type="isbn">978-3-030-59710-8</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/978-3-030-59710-8_3</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, online, 04.-08.10.2020</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/1257</enrichment>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Anne</first_name>
      <last_name>Petrick</last_name>
    </author>
    <author>
      <first_name>Kristina</first_name>
      <last_name>Weiler</last_name>
    </author>
    <author>
      <first_name>Agnes C.</first_name>
      <last_name>Gläsel</last_name>
    </author>
    <author>
      <first_name>Marco</first_name>
      <last_name>Fragoso-Garcia</last_name>
    </author>
    <author>
      <first_name>Sophie</first_name>
      <last_name>Merz</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Bartenschlager</last_name>
    </author>
    <author>
      <first_name>Judith</first_name>
      <last_name>Hoppe</last_name>
    </author>
    <author>
      <first_name>Alina</first_name>
      <last_name>Langenhagen</last_name>
    </author>
    <author>
      <first_name>Anne-Katherine</first_name>
      <last_name>Jasensky</last_name>
    </author>
    <author>
      <first_name>Jörn</first_name>
      <last_name>Voigt</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pathology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Microscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computer-aided labelling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Expert-algorithm collaboration</value>
    </subject>
  </doc>
  <doc>
    <id>1252</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>13</pageFirst>
    <pageLast>22</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Singapore</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-02-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Classification of leukemic b-lymphoblast cells from blood smear microscopic images with an attention-based deep learning method and advanced augmentation techniques</title>
    <parentTitle language="eng">ISBI 2019 C-NMC challenge: classification in cancer cell imaging</parentTitle>
    <identifier type="isbn">978-981-15-0797-7</identifier>
    <identifier type="isbn">978-981-15-0798-4</identifier>
    <identifier type="isbn">978-981-15-0800-4</identifier>
    <identifier type="issn">2195-271X</identifier>
    <identifier type="issn">2195-2728</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/978-981-15-0798-4_2</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">IEEE International Symposium on Biomedical Imaging, Venice (Italy), 8.-11.04.2019</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Jörn</first_name>
      <last_name>Voigt</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
  </doc>
  <doc>
    <id>1459</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>26</pageFirst>
    <pageLast>31</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>Springer Vieweg</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-02-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Is crowd-algorithm collaboration an advanced alternative to crowd-sourcing on cytology slides?</title>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2020, Algorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin</parentTitle>
    <identifier type="isbn">978-3-658-29266-9</identifier>
    <identifier type="isbn">978-3-658-29267-6</identifier>
    <note>Access to this content is enabled by Nationallizenz Ebooks Medicine</note>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/978-3-658-29267-6_5</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">Bildverarbeitung für die Medizin 2020, Berlin (Germany), 15.-17.03.2020</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <editor>
      <first_name>Thomas</first_name>
      <last_name>Tolxdorff</last_name>
    </editor>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <editor>
      <first_name>Thomas Martin</first_name>
      <last_name>Deserno</last_name>
    </editor>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <editor>
      <first_name>Heinz</first_name>
      <last_name>Handels</last_name>
    </editor>
    <author>
      <first_name>Stefan</first_name>
      <last_name>Gerlach</last_name>
    </author>
    <editor>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </editor>
    <author>
      <first_name>Jennifer</first_name>
      <last_name>Maier</last_name>
    </author>
    <editor>
      <first_name>Klaus H.</first_name>
      <last_name>Maier-Hein</last_name>
    </editor>
    <author>
      <first_name>Jörn</first_name>
      <last_name>Voigt</last_name>
    </author>
    <editor>
      <first_name>Christoph</first_name>
      <last_name>Palm</last_name>
    </editor>
    <author>
      <first_name>Jenny</first_name>
      <last_name>Hill</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
  </doc>
  <doc>
    <id>1439</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>147</pageFirst>
    <pageLast>152</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>Springer Vieweg</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Cell detection for asthma on partially annotated whole slide images</title>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop&#13;
on Medical Image Computing, Regensburg, March 7–9, 2021</parentTitle>
    <subTitle language="eng">learning to be EXACT</subTitle>
    <identifier type="issn">1431-472X</identifier>
    <identifier type="isbn">978-3-658-33197-9</identifier>
    <identifier type="isbn">978-3-658-33198-6</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/978-3-658-33198-6_36</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">Bildverarbeitung für die Medizin 2021 (German Workshop on Medical Image Computing) Regensburg (Germany), 07.-09.03.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Frauke</first_name>
      <last_name>Wilm</last_name>
    </author>
    <author>
      <first_name>Jörn</first_name>
      <last_name>Voigt</last_name>
    </author>
    <author>
      <first_name>Ann K.</first_name>
      <last_name>Barton</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
  </doc>
  <doc>
    <id>1178</id>
    <completedYear>2020</completedYear>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume>10</volume>
    <articleNumber>9795</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-02-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Deep Learning-based quantification of pulmonary hemosiderophages in cytology slides</title>
    <abstract language="eng">Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter- and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages. Our deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordance (0.68 to 0.86, mean of 0.73, SD of 0.04). Intra-observer variability was high (0.68 to 0.88) and inter-observer concordance was moderate (Fleiss’ kappa = 0.67). Our object detection approach has a mean average precision of 0.66 over the five classes from the whole slide gigapixel image and a computation time of below two minutes. To mitigate the high inter- and intra-rater variability, we propose our automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-11783</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1038/s41598-020-65958-2</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Jason</first_name>
      <last_name>Stayt</last_name>
    </author>
    <author>
      <first_name>Anne-Katherine</first_name>
      <last_name>Jasensky</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Bartenschlager</last_name>
    </author>
    <author>
      <first_name>Marco</first_name>
      <last_name>Fragoso-Garcia</last_name>
    </author>
    <author>
      <first_name>Ann K.</first_name>
      <last_name>Barton</last_name>
    </author>
    <author>
      <first_name>Svenja</first_name>
      <last_name>Elsemann</last_name>
    </author>
    <author>
      <first_name>Samir</first_name>
      <last_name>Jabari</last_name>
    </author>
    <author>
      <first_name>Jens</first_name>
      <last_name>Krauth</last_name>
    </author>
    <author>
      <first_name>Prathmesh</first_name>
      <last_name>Madhu</last_name>
    </author>
    <author>
      <first_name>Jörn</first_name>
      <last_name>Voigt</last_name>
    </author>
    <author>
      <first_name>Jenny</first_name>
      <last_name>Hill</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1178/s41598-020-65958-2.pdf</file>
  </doc>
  <doc>
    <id>1094</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue/>
    <volume>11</volume>
    <articleNumber>4343</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-01-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control</title>
    <abstract language="eng">In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-10943</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1038/s41598-021-83827-4</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Christian</first_name>
      <last_name>Marzahl</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Christof</first_name>
      <last_name>Bertram</last_name>
    </author>
    <author>
      <first_name>Jennifer</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Bergler</last_name>
    </author>
    <author>
      <first_name>Christine</first_name>
      <last_name>Kröger</last_name>
    </author>
    <author>
      <first_name>Jörn</first_name>
      <last_name>Voigt</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Klopfleisch</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1094/s41598-021-83827-4.pdf</file>
  </doc>
</export-example>
