Jahanifar, Mostafa, Shephard, Adam, Zamanitajeddin, Neda, Bashir, R. M. Saad, Bilal, Mohsin, Khurram, Syed Ali, Minhas, Fayyaz and Rajpoot, Nasir (2022) Stain-robust mitotic figure detection for the mitosis domain generalization challenge. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France, 27 Sep-01 Oct 2021. Published in: Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 13166 (1). pp. 48-52. ISBN 9783030972806. doi:10.1007/978-3-030-97281-3_6 ISSN 0302-9743.

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Abstract

The detection of mitotic figures from different scanners/sites remains an important topic of research, owing to its potential in assisting clinicians with tumour grading. The MItosis DOmain Generalization (MIDOG) challenge aims to test the robustness of detection models on unseen data from multiple scanners for this task. We present a short summary of the approach employed by the TIA Centre team to address this challenge. Our approach is based on a hybrid detection model, where mitotic candidates are segmented on stain normalised images, before being refined by a deep learning classifier. Cross-validation on the training images achieved the F1-score of 0.786 and 0.765 on the preliminary test set, demonstrating the generalizability of our model to unseen data from new scanners.

Item Type:	Conference Item (Paper)
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Series Name:	Lecture Notes in Computer Science
Journal or Publication Title:	Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis
Publisher:	Springer Cham
ISBN:	9783030972806
ISSN:	0302-9743
Book Title:	Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis
Official Date:	2 March 2022
Dates:	Date Event 2 March 2022 Published
Volume:	13166
Number:	1
Page Range:	pp. 48-52
DOI:	10.1007/978-3-030-97281-3_6
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	The final authenticated version is available online at https://doi.org/10.1007/978-3-030-97281-3_6
Access rights to Published version:	Restricted or Subscription Access
Copyright Holders:	Springer Nature Switzerland AG
Conference Paper Type:	Paper
Title of Event:	International Conference on Medical Image Computing and Computer-Assisted Intervention
Type of Event:	Conference
Location of Event:	Strasbourg, France
Date(s) of Event:	27 Sep-01 Oct 2021
Related URLs:	Organisation
Open Access Version:	ArXiv

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