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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 30 / No. 1: Thirtieth AAAI Conference On Artificial Intelligence

Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks

March 8, 2023

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Authors

Hao Chen

The Chinese University of Hong Kong


Qi Dou

The Chinese University of Hong Kong


Xi Wang

Sichuan Univerisity


Jing Qin

Shenzhen University


Pheng Heng

The Chinese University of Hong Kong


DOI:

10.1609/aaai.v30i1.10140


Abstract:

The number of mitoses per tissue area gives an important aggressiveness indication of the invasive breast carcinoma.However, automatic mitosis detection in histology images remains a challenging problem. Traditional methods either employ hand-crafted features to discriminate mitoses from other cells or construct a pixel-wise classifier to label every pixel in a sliding window way. While the former suffers from the large shape variation of mitoses and the existence of many mimics with similar appearance, the slow speed of the later prohibits its use in clinical practice.In order to overcome these shortcomings, we propose a fast and accurate method to detect mitosis by designing a novel deep cascaded convolutional neural network, which is composed of two components. First, by leveraging the fully convolutional neural network, we propose a coarse retrieval model to identify and locate the candidates of mitosis while preserving a high sensitivity.Based on these candidates, a fine discrimination model utilizing knowledge transferred from cross-domain is developed to further single out mitoses from hard mimics.Our approach outperformed other methods by a large margin in 2014 ICPR MITOS-ATYPIA challenge in terms of detection accuracy. When compared with the state-of-the-art methods on the 2012 ICPR MITOSIS data (a smaller and less challenging dataset), our method achieved comparable or better results with a roughly 60 times faster speed.

Topics: AAAI

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HOW TO CITE:

Hao Chen|| Qi Dou|| Xi Wang|| Jing Qin|| Pheng Heng Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks Proceedings of the AAAI Conference on Artificial Intelligence, 30 (2016) .

Hao Chen|| Qi Dou|| Xi Wang|| Jing Qin|| Pheng Heng Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks AAAI 2016, .

Hao Chen|| Qi Dou|| Xi Wang|| Jing Qin|| Pheng Heng (2016). Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 30, .

Hao Chen|| Qi Dou|| Xi Wang|| Jing Qin|| Pheng Heng. Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 30 2016 p..

Hao Chen|| Qi Dou|| Xi Wang|| Jing Qin|| Pheng Heng. 2016. Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks. "Proceedings of the AAAI Conference on Artificial Intelligence, 30". .

Hao Chen|| Qi Dou|| Xi Wang|| Jing Qin|| Pheng Heng. (2016) "Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks", Proceedings of the AAAI Conference on Artificial Intelligence, 30, p.

Hao Chen|| Qi Dou|| Xi Wang|| Jing Qin|| Pheng Heng, "Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks", AAAI, p., 2016.

Hao Chen|| Qi Dou|| Xi Wang|| Jing Qin|| Pheng Heng. "Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 30, 2016, p..

Hao Chen|| Qi Dou|| Xi Wang|| Jing Qin|| Pheng Heng. "Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 30, (2016): .

Hao Chen|| Qi Dou|| Xi Wang|| Jing Qin|| Pheng Heng. Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks. AAAI[Internet]. 2016[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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