Skip to content

Commit

Permalink
Merge pull request #5916 from openjournals/joss.07022
Browse files Browse the repository at this point in the history 
Merging automatically
  • Loading branch information
@editorialbot
editorialbot authored Sep 26, 2024
2 parents 2fdedd3 + 6dcfbc6 commit 2cac0b1
Show file tree
Hide file tree
Showing 7 changed files with 1,068 additions and 0 deletions.
313 changes: 313 additions & 0 deletions joss.07022/10.21105.joss.07022.crossref.xml
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,313 @@
<?xml version="1.0" encoding="UTF-8"?>
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1"
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd"
xmlns:rel="http://www.crossref.org/relations.xsd"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
version="5.3.1"
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd">
<head>
<doi_batch_id>20240926124722-785bb831e82d8e9a631277e057c3e52d0a470e41</doi_batch_id>
<timestamp>20240926124722</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>admin@theoj.org</email_address>
</depositor>
<registrant>The Open Journal</registrant>
</head>
<body>
<journal>
<journal_metadata>
<full_title>Journal of Open Source Software</full_title>
<abbrev_title>JOSS</abbrev_title>
<issn media_type="electronic">2475-9066</issn>
<doi_data>
<doi>10.21105/joss</doi>
<resource>https://joss.theoj.org</resource>
</doi_data>
</journal_metadata>
<journal_issue>
<publication_date media_type="online">
<month>09</month>
<year>2024</year>
</publication_date>
<journal_volume>
<volume>9</volume>
</journal_volume>
<issue>101</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>HoverFast: an accurate, high-throughput, clinically
deployable nuclear segmentation tool for brightfield digital pathology
images</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Petros</given_name>
<surname>Liakopoulos</surname>
<ORCID>https://orcid.org/0009-0005-2015-6795</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Julien</given_name>
<surname>Massonnet</surname>
<ORCID>https://orcid.org/0009-0004-9515-6100</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Jonatan</given_name>
<surname>Bonjour</surname>
<ORCID>https://orcid.org/0009-0006-8165-6897</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Medya Tekes</given_name>
<surname>Mizrakli</surname>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Simon</given_name>
<surname>Graham</surname>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Michel A.</given_name>
<surname>Cuendet</surname>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Amanda H.</given_name>
<surname>Seipel</surname>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Olivier</given_name>
<surname>Michielin</surname>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Doron</given_name>
<surname>Merkler</surname>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Andrew</given_name>
<surname>Janowczyk</surname>
</person_name>
</contributors>
<publication_date>
<month>09</month>
<day>26</day>
<year>2024</year>
</publication_date>
<pages>
<first_page>7022</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.07022</identifier>
</publisher_item>
<ai:program name="AccessIndicators">
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
</ai:program>
<rel:program>
<rel:related_item>
<rel:description>Software archive</rel:description>
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.13838349</rel:inter_work_relation>
</rel:related_item>
<rel:related_item>
<rel:description>GitHub review issue</rel:description>
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/7022</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.07022</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.07022</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.07022.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="schmidt2018">
<article_title>Cell detection with star-convex
polygons</article_title>
<author>Schmidt</author>
<journal_title>Medical image computing and computer assisted
intervention - MICCAI 2018 - 21st international conference, granada,
spain, september 16-20, 2018, proceedings, part II</journal_title>
<doi>10.1007/978-3-030-00934-2_30</doi>
<cYear>2018</cYear>
<unstructured_citation>Schmidt, U., Weigert, M., Broaddus,
C., &amp; Myers, G. (2018). Cell detection with star-convex polygons.
Medical Image Computing and Computer Assisted Intervention - MICCAI 2018
- 21st International Conference, Granada, Spain, September 16-20, 2018,
Proceedings, Part II, 265–273.
https://doi.org/10.1007/978-3-030-00934-2_30</unstructured_citation>
</citation>
<citation key="weigert2020">
<article_title>Star-convex polyhedra for 3D object detection
and segmentation in microscopy</article_title>
<author>Weigert</author>
<journal_title>The IEEE winter conference on applications of
computer vision (WACV)</journal_title>
<doi>10.1109/WACV45572.2020.9093435</doi>
<cYear>2020</cYear>
<unstructured_citation>Weigert, M., Schmidt, U., Haase, R.,
Sugawara, K., &amp; Myers, G. (2020). Star-convex polyhedra for 3D
object detection and segmentation in microscopy. The IEEE Winter
Conference on Applications of Computer Vision (WACV).
https://doi.org/10.1109/WACV45572.2020.9093435</unstructured_citation>
</citation>
<citation key="stringer2021cellpose">
<article_title>Cellpose: A generalist algorithm for cellular
segmentation</article_title>
<author>Stringer</author>
<journal_title>Nature methods</journal_title>
<issue>1</issue>
<volume>18</volume>
<doi>10.1038/s41592-020-01018-x</doi>
<cYear>2021</cYear>
<unstructured_citation>Stringer, C., Wang, T., Michaelos,
M., &amp; Pachitariu, M. (2021). Cellpose: A generalist algorithm for
cellular segmentation. Nature Methods, 18(1), 100–106.
https://doi.org/10.1038/s41592-020-01018-x</unstructured_citation>
</citation>
<citation key="graham2019hover">
<article_title>Hover-net: Simultaneous segmentation and
classification of nuclei in multi-tissue histology
images</article_title>
<author>Graham</author>
<journal_title>Medical image analysis</journal_title>
<volume>58</volume>
<doi>10.1016/j.media.2019.101563</doi>
<cYear>2019</cYear>
<unstructured_citation>Graham, S., Vu, Q. D., Raza, S. E.
A., Azam, A., Tsang, Y. W., Kwak, J. T., &amp; Rajpoot, N. (2019).
Hover-net: Simultaneous segmentation and classification of nuclei in
multi-tissue histology images. Medical Image Analysis, 58, 101563.
https://doi.org/10.1016/j.media.2019.101563</unstructured_citation>
</citation>
<citation key="bankhead2017qupath">
<article_title>QuPath: Open source software for digital
pathology image analysis</article_title>
<author>Bankhead</author>
<journal_title>Scientific reports</journal_title>
<issue>1</issue>
<volume>7</volume>
<doi>10.1038/s41598-017-17204-5</doi>
<cYear>2017</cYear>
<unstructured_citation>Bankhead, P., Loughrey, M. B.,
Fernández, J. A., Dombrowski, Y., McArt, D. G., Dunne, P. D., McQuaid,
S., Gray, R. T., Murray, L. J., Coleman, H. G., &amp; others. (2017).
QuPath: Open source software for digital pathology image analysis.
Scientific Reports, 7(1), 1–7.
https://doi.org/10.1038/s41598-017-17204-5</unstructured_citation>
</citation>
<citation key="su2021msu">
<article_title>MSU-net: Multi-scale u-net for 2D medical
image segmentation</article_title>
<author>Su</author>
<journal_title>Frontiers in Genetics</journal_title>
<volume>12</volume>
<doi>10.3389/fgene.2021.639930</doi>
<cYear>2021</cYear>
<unstructured_citation>Su, R., Zhang, D., Liu, J., &amp;
Cheng, C. (2021). MSU-net: Multi-scale u-net for 2D medical image
segmentation. Frontiers in Genetics, 12, 639930.
https://doi.org/10.3389/fgene.2021.639930</unstructured_citation>
</citation>
<citation key="paszke2017automatic">
<article_title>Automatic differentiation in
PyTorch</article_title>
<author>Paszke</author>
<cYear>2017</cYear>
<unstructured_citation>Paszke, A., Gross, S., Chintala, S.,
Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L.,
&amp; Lerer, A. (2017). Automatic differentiation in
PyTorch.</unstructured_citation>
</citation>
<citation key="van_der_Walt_2014">
<article_title>Scikit-image: Image processing in
python</article_title>
<author>Walt</author>
<journal_title>PeerJ</journal_title>
<volume>2</volume>
<doi>10.7717/peerj.453</doi>
<issn>2167-8359</issn>
<cYear>2014</cYear>
<unstructured_citation>Walt, S. van der, Schönberger, J. L.,
Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart,
E., &amp; Yu, T. (2014). Scikit-image: Image processing in python.
PeerJ, 2, e453.
https://doi.org/10.7717/peerj.453</unstructured_citation>
</citation>
<citation key="opencv_library">
<article_title>The OpenCV Library</article_title>
<author>Bradski</author>
<journal_title>Dr. Dobb’s Journal of Software
Tools</journal_title>
<cYear>2000</cYear>
<unstructured_citation>Bradski, G. (2000). The OpenCV
Library. Dr. Dobb’s Journal of Software Tools.</unstructured_citation>
</citation>
<citation key="weinstein2013cancer">
<article_title>The cancer genome atlas pan-cancer analysis
project</article_title>
<author>Weinstein</author>
<journal_title>Nature genetics</journal_title>
<issue>10</issue>
<volume>45</volume>
<doi>10.1038/ng.2764</doi>
<cYear>2013</cYear>
<unstructured_citation>Weinstein, J. N., Collisson, E. A.,
Mills, G. B., Shaw, K. R., Ozenberger, B. A., Ellrott, K., Shmulevich,
I., Sander, C., &amp; Stuart, J. M. (2013). The cancer genome atlas
pan-cancer analysis project. Nature Genetics, 45(10), 1113–1120.
https://doi.org/10.1038/ng.2764</unstructured_citation>
</citation>
<citation key="hinton2015distilling">
<article_title>Distilling the knowledge in a neural
network</article_title>
<author>Hinton</author>
<journal_title>arXiv preprint
arXiv:1503.02531</journal_title>
<cYear>2015</cYear>
<unstructured_citation>Hinton, G., Vinyals, O., &amp; Dean,
J. (2015). Distilling the knowledge in a neural network. arXiv Preprint
arXiv:1503.02531.</unstructured_citation>
</citation>
<citation key="hu2022teacher">
<article_title>Teacher-student architecture for knowledge
learning: A survey</article_title>
<author>Hu</author>
<journal_title>arXiv preprint
arXiv:2210.17332</journal_title>
<cYear>2022</cYear>
<unstructured_citation>Hu, C., Li, X., Liu, D., Chen, X.,
Wang, J., &amp; Liu, X. (2022). Teacher-student architecture for
knowledge learning: A survey. arXiv Preprint
arXiv:2210.17332.</unstructured_citation>
</citation>
<citation key="janowczyk2019histoqc">
<article_title>HistoQC: An open-source quality control tool
for digital pathology slides</article_title>
<author>Janowczyk</author>
<journal_title>JCO clinical cancer
informatics</journal_title>
<volume>3</volume>
<doi>10.1200/CCI.18.00157</doi>
<cYear>2019</cYear>
<unstructured_citation>Janowczyk, A., Zuo, R., Gilmore, H.,
Feldman, M., &amp; Madabhushi, A. (2019). HistoQC: An open-source
quality control tool for digital pathology slides. JCO Clinical Cancer
Informatics, 3, 1–7.
https://doi.org/10.1200/CCI.18.00157</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Binary file added joss.07022/10.21105.joss.07022.pdf
View file
Binary file not shown.
Loading

0 comments on commit 2cac0b1

Please sign in to comment.