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Authors: Tirupati Chandra ; Sahar Nasser ; Nikhil Kurian and Amit Sethi

Affiliation: Electrical Engineering Department, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

Keyword(s): MIDOG, Domain Generalization, Mitosis Detection, Domain Homogenizer, Auto-Encoder.

Abstract: The effective counting of mitotic figures in cancer pathology specimen is a critical task for deciding tumor grade and prognosis. Automated mitosis detection through deep learning-based image analysis often fails on unseen patient data due to domain shifts in the form of changes in stain appearance, pixel noise, tissue quality, and magnification. This paper proposes a domain homogenizer for mitosis detection that attempts to alleviate domain differences in histology images via adversarial reconstruction of input images. The proposed homogenizer is based on a U-Net architecture and can effectively reduce domain differences commonly seen with histology imaging data. We demonstrate our domain homogenizer’s effectiveness by showing a reduction in domain differences between the preprocessed images. Using this homogenizer with a RetinaNet object detector, we were able to outperform the baselines of the 2021 MIDOG challenge in terms of average precision of the detected mitotic figures.

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Paper citation in several formats:
Chandra, T.; Nasser, S.; Kurian, N. and Sethi, A. (2023). Improving Mitosis Detection via UNet-Based Adversarial Domain Homogenizer. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOIMAGING; ISBN 978-989-758-631-6; ISSN 2184-4305, SciTePress, pages 52-56. DOI: 10.5220/0011629700003414

@conference{bioimaging23,
author={Tirupati Chandra. and Sahar Nasser. and Nikhil Kurian. and Amit Sethi.},
title={Improving Mitosis Detection via UNet-Based Adversarial Domain Homogenizer},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOIMAGING},
year={2023},
pages={52-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011629700003414},
isbn={978-989-758-631-6},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOIMAGING
TI - Improving Mitosis Detection via UNet-Based Adversarial Domain Homogenizer
SN - 978-989-758-631-6
IS - 2184-4305
AU - Chandra, T.
AU - Nasser, S.
AU - Kurian, N.
AU - Sethi, A.
PY - 2023
SP - 52
EP - 56
DO - 10.5220/0011629700003414
PB - SciTePress

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