Parallel YOLO-based Model for Real-time Mitosis Counting

Yancey, Robin

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DC Field	Value	Language
dc.contributor.author	Yancey, Robin
dc.contributor.editor	Skala, Václav
dc.date.accessioned	2022-09-02T08:33:22Z	-
dc.date.available	2022-09-02T08:33:22Z	-
dc.date.issued	2023
dc.identifier.citation	WSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 256-264.	en
dc.identifier.isbn	978-80-86943-33-6
dc.identifier.issn	2464-4617
dc.identifier.uri	http://hdl.handle.net/11025/49602
dc.format	9 s.	cs
dc.format.mimetype	application/pdf
dc.language.iso	en	en
dc.publisher	Václav Skala - UNION Agency	en
dc.rights	© Václav Skala - UNION Agency	en
dc.subject	YOLO	cs
dc.subject	hluboké učení	cs
dc.subject	počítání mitózy	cs
dc.subject	rakovina prsu	cs
dc.subject	histopatologie	cs
dc.subject	strojové učení	cs
dc.subject	detekce v reálném čase	cs
dc.title	Parallel YOLO-based Model for Real-time Mitosis Counting	en
dc.type	conferenceObject	en
dc.rights.access	openAccess	en
dc.type.version	publishedVersion	en
dc.description.abstract-translated	It is estimated that breast cancer incidences will increase by more than 50% by 2030 from 2011. Mitosis counting is one of the most commonly used methods of assessing the level of progression, and is a routine task for every patient diagnosed with invasive cancer. Although mitotic count is the strongest prognostic value, it is a tedious and subjective task with poor reproducibility, especially for non-experts. Object detection networks such as Faster RCNN have recently been adapted to medical applications to automatically localize regions of interest better than a CNN alone. However, the speed and accuracy of newer state-of-the-art models such as YOLO are now leaders in object detection, which had yet be applied to mitosis counting. Moreover, combining results of multiple YOLO versions run in parallel and increasing the size of the data in a way that is appropriate for the specific task are some of the other methods can be used to further improve the score overall. Using these techniques the highest F-scores of 0.95 and 0.96 on the MITOS-ATYPIA 2014 challenge and MITOS-ATYPIA 2012 challenge mitosis counting datasets are achieved, respectively.	en
dc.subject.translated	YOLO	en
dc.subject.translated	deep learning	en
dc.subject.translated	mitosis counting	en
dc.subject.translated	breast cancer	en
dc.subject.translated	histopathology	en
dc.subject.translated	machine learning	en
dc.subject.translated	real-time detection	en
dc.identifier.doi	https://www.doi.org/10.24132/CSRN.3201.32
dc.type.status	Peer-reviewed	en
Appears in Collections:	WSCG 2022: Full Papers Proceedings

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