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Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features

  • 1176: Artificial Intelligence and Deep Learning for Biomedical Applications
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Abstract

One of the most important processes in the diagnosis of breast cancer, which is the leading mortality rate in women, is the detection of the mitosis stage at the cellular level. In literature, many studies have been proposed on the computer-aided diagnosis (CAD) system for detecting mitotic cells in breast cancer histopathological images. In this study, comparative evaluation of conventional and deep learning based feature extraction methods for automatic detection of mitosis in histopathological images are focused. While various handcrafted features are extracted with textural/spatial, statistical and shape-based methods in conventional approach, the convolutional neural network structure proposed on the deep learning approach aims to create an architecture that extracts the features of small cellular structures such as mitotic cells. Mitosis detection/counting is an important process that helps us assess how aggressive or malignant the cancer’s spread is. In the proposed study, approximately 180,000 non-mitotic and 748 mitotic cells are extracted for the evaluations. It is obvious that the classification stage cannot be performed properly due to the imbalanced numbers of mitotic and non-mitotic cells extracted from histopathological images. Hence, the random under-sampling boosting (RUSBoost) method is exploited to overcome this problem. The proposed framework is tested on mitosis detection in breast cancer histopathological images dataset provided from the International Conference on Pattern Recognition (ICPR) 2014 contest. In the results obtained with the deep learning approach, 79.42% recall, 96.78% precision and 86.97% F-measure values are achieved more successfully than handcrafted methods. A client/server-based framework has also been developed as a secondary decision support system for use by pathologists in hospitals. Thus, it is aimed that pathologists will be able to detect mitotic cells in various histopathological images more easily through necessary interfaces.

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Acknowledgements

This work was supported by Yildiz Technical University, Scientific Research Projects Coordination Department, Project Number: 2014-04-01-KAP01.

We also thank to organizers of Mitosis-Atypia-2014 contest and providers of dataset released in International Conference of Pattern Recognition, ICPR’14.

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Authors and Affiliations

  1. Department of Computer Engineering, YTU, 34220 Istanbul, Turkey and SIMPLAB in YTU, 34220, Istanbul, Turkey

    I. Onur Sigirci

  2. Department of Computer Engineering, Dicle University, 21280 Diyarbakir, Turkey and SIMPLAB in YTU, 34220, Istanbul, Turkey

    Abdulkadir Albayrak

  3. Department of Computer Engineering, Yildiz Technical University (YTU), 34220 Istanbul, Turkey and Signal and Image Processing Lab. (SIMPLAB) in YTU, 34220, Istanbul, Turkey

    Gokhan Bilgin

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  1. I. Onur Sigirci
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Correspondence to Gokhan Bilgin.

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Sigirci, I.O., Albayrak, A. & Bilgin, G. Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features. Multimed Tools Appl 81, 13179–13202 (2022). https://doi.org/10.1007/s11042-021-10539-2

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