Mitosisnet: End-to-End Mitotic Cell Detection by Multi-Task Learning

Author(s)

Md Zahangir Alom (0000-0002-2314-1207), University of DaytonFollow
Theus Aspiras, University of DaytonFollow
Tarek M. Taha, University of DaytonFollow
Tj Bowen, Deep Lens Inc
Vijayan K. Asari, University of DaytonFollow

Document Type

Article

Publication Date

2020

Publication Source

IEEE Access

Abstract

Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis. The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist. Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice. In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named"MitosisNet". MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region detection and the classification model is applied for further confirmation of the mitosis regions. In addition, an integrated multi-patch reference scheme and a novel confidence analysis strategy are introduced for improving overall detection performance during testing. The proposed system is evaluated on three different publicly available datasets including MITOSIS 2012, MITOSIS 2014, and Case Western Reserve University (CWRU) datasets. The experimental results demonstrate state- of-the-art performance compared to the existing methods and the proposed approach is fast enough in order to meet the requirements of clinical practice.

Inclusive pages

68695-68710

ISBN/ISSN

2169-3536

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1109/ACCESS.2020.2983995

Publisher

IEEE-Inst Electrical Electronics Engineers INC

Volume

8

Peer Reviewed

yes

Sponsoring Agency

Deep Lens Inc

eCommons Citation

Alom, Md Zahangir; Aspiras, Theus; Taha, Tarek M.; Bowen, Tj; and Asari, Vijayan K., "Mitosisnet: End-to-End Mitotic Cell Detection by Multi-Task Learning" (2020). Electrical and Computer Engineering Faculty Publications. 448.
https://ecommons.udayton.edu/ece_fac_pub/448