Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge

Xi Long; Ying Cheng; Xiao Mu; Lian Liu; Jingxin Liu

doi:10.1007/978-3-030-97281-3_11

Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge

Xi Long, Ying Cheng, Xiao Mu, Lian Liu, Jingxin Liu^*

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

2 Citations (Scopus)

Abstract

We present a summary of domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images. By comprehensive data augmentation and adapting existing popular detection architecture, our proposed method has achieved an F1 score of 0.7500 on the preliminary test set in MItosis DOmain Generalization (MIDOG) Challenge at MICCAI 2021.

Original language	English
Title of host publication	Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis - MICCAI 2021 Challenges
Subtitle of host publication	MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Proceedings
Editors	Marc Aubreville, David Zimmerer, Mattias Heinrich
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	73-76
Number of pages	4
ISBN (Print)	9783030972806
DOIs	https://doi.org/10.1007/978-3-030-97281-3_11
Publication status	Published - 2022
Externally published	Yes
Event	24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 - Strasbourg, France Duration: 27 Sept 2021 → 1 Oct 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13166 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021
Country/Territory	France
City	Strasbourg
Period	27/09/21 → 1/10/21

Keywords

Domain Adaptation
Histopathology
Mitosis detection

Access to Document

10.1007/978-3-030-97281-3_11

Cite this

Long, X., Cheng, Y., Mu, X., Liu, L., & Liu, J. (2022). Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge. In M. Aubreville, D. Zimmerer, & M. Heinrich (Eds.), Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis - MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Proceedings (pp. 73-76). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13166 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-97281-3_11

Long, Xi ; Cheng, Ying ; Mu, Xiao et al. / Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge. Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis - MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Proceedings. editor / Marc Aubreville ; David Zimmerer ; Mattias Heinrich. Springer Science and Business Media Deutschland GmbH, 2022. pp. 73-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge",

abstract = "We present a summary of domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images. By comprehensive data augmentation and adapting existing popular detection architecture, our proposed method has achieved an F1 score of 0.7500 on the preliminary test set in MItosis DOmain Generalization (MIDOG) Challenge at MICCAI 2021.",

keywords = "Domain Adaptation, Histopathology, Mitosis detection",

author = "Xi Long and Ying Cheng and Xiao Mu and Lian Liu and Jingxin Liu",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 01-10-2021",

year = "2022",

doi = "10.1007/978-3-030-97281-3_11",

language = "English",

isbn = "9783030972806",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

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editor = "Marc Aubreville and David Zimmerer and Mattias Heinrich",

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Long, X, Cheng, Y, Mu, X, Liu, L & Liu, J 2022, Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge. in M Aubreville, D Zimmerer & M Heinrich (eds), Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis - MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13166 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 73-76, 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, Strasbourg, France, 27/09/21. https://doi.org/10.1007/978-3-030-97281-3_11

Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge. / Long, Xi; Cheng, Ying; Mu, Xiao et al.
Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis - MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Proceedings. ed. / Marc Aubreville; David Zimmerer; Mattias Heinrich. Springer Science and Business Media Deutschland GmbH, 2022. p. 73-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13166 LNCS).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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Long X, Cheng Y, Mu X, Liu L, Liu J. Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge. In Aubreville M, Zimmerer D, Heinrich M, editors, Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis - MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 73-76. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-97281-3_11