AUTHOR=Li Zhiqiang , Li Xiangkui , Wu Weixuan , Lyu He , Tang Xuezhi , Zhou Chenchen , Xu Fanxin , Luo Bin , Jiang Yulian , Liu Xingwen , Xiang Wei
TITLE=A novel dilated contextual attention module for breast cancer mitosis cell detection
JOURNAL=Frontiers in Physiology
VOLUME=15
YEAR=2024
URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2024.1337554
DOI=10.3389/fphys.2024.1337554
ISSN=1664-042X
ABSTRACT=Background and object: Mitotic count (MC) is a critical histological parameter for accurately assessing the degree of invasiveness in breast cancer, holding significant clinical value for cancer treatment and prognosis. However, accurately identifying mitotic cells poses a challenge due to their morphological and size diversity.
Objective: We propose a novel end-to-end deep-learning method for identifying mitotic cells in breast cancer pathological images, with the aim of enhancing the performance of recognizing mitotic cells.
Methods: We introduced the Dilated Cascading Network (DilCasNet) composed of detection and classification stages. To enhance the model’s ability to capture distant feature dependencies in mitotic cells, we devised a novel Dilated Contextual Attention Module (DiCoA) that utilizes sparse global attention during the detection. For reclassifying mitotic cell areas localized in the detection stage, we integrate the EfficientNet-B7 and VGG16 pre-trained models (InPreMo) in the classification step.
Results: Based on the canine mammary carcinoma (CMC) mitosis dataset, DilCasNet demonstrates superior overall performance compared to the benchmark model. The specific metrics of the model’s performance are as follows: F1 score of 82.9%, Precision of 82.6%, and Recall of 83.2%. With the incorporation of the DiCoA attention module, the model exhibited an improvement of over 3.5% in the F1 during the detection stage.
Conclusion: The DilCasNet achieved a favorable detection performance of mitotic cells in breast cancer and provides a solution for detecting mitotic cells in pathological images of other cancers.