A novel multilevel iterative training strategy for the ResNet50 based mitotic cell classifier

Abstract

The number of mitotic cells is an important indicator of grading invasive breast cancer. It is very challenging for pathologists to identify and count mitotic cells in pathological sections with naked eyes under the microscope. Therefore, many computational models for the automatic identification of mitotic cells based on machine learning, especially deep learning, have been proposed. However, converging to the local optimal solution is one of the main problems in model training. In this paper, we proposed a novel multilevel iterative training strategy to address the problem. To evaluate the proposed training strategy, we constructed the mitotic cell classification model with ResNet50 and trained the model with different training strategies. The results showed that the models trained with the proposed training strategy performed better than those trained with the conventional strategy in the independent test set, illustrating the effectiveness of the new training strategy. Furthermore, after training with our proposed strategy, the ResNet50 model with Adam optimizer has achieved 89.26% F1 score on the public MITOSI14 dataset, which is higher than that of the state-of-the-art methods reported in the literature.

Keywords: Deep learning; Invasive breast cancer; Mitotic cell; Multi-level iterative training strategy; ResNet50.