Deep learning-based immunohistochemical estimation of breast cancer via ultrasound image applications - Yan, Ding; Zhao, Zijian; Duan, Jiajun; Qu, Jia; Shi, Linlin; Wang, Qian; Zhang, Huawei (Frontiers in Oncology, 2024)
Breast cancer is the key global menace to female’s health, ranking first by the mortality rate. This rate reduction and early diagnostics of breast cancer are the mainstream of medical research. Immunohistochemical examination is the most important link in the process of breast cancer treatment, and its results directly affect physicians’ decision-making on follow-up medical treatment. This study aims to develop a computer-aided diagnosis (CAD) method based on deep learning to classify breast ultrasound (BUS) images according to immunohistochemical results. A new depth learning framework guided by BUS image data analysis was proposed for the classification of breast cancer nodes in BUS images. The proposed CAD classification network mainly comprised three innovation points. First, a multi-level feature distillation network (MFD-Net) based on CNN, which could extract feature layers of different scales, was designed. Then, the image features extracted at different depths were fused to achieve multi-level feature distillation using depth-separable convolution and reverse-depth-separable convolution to increase convolution depths. Finally, a new attention module containing two independent sub-modules, the Channel Attention Module (CAM) and Spatial Attention Module (SAM), was introduced to improve the model classification ability in channel and space. A total of 500 axial BUS images were retrieved from 294 patients who underwent BUS examination, and these images were detected and cropped, resulting in a breast cancer node BUS image datasets, which were classified according to immunohistochemical findings, and the datasets were randomly subdivided into a training set (70%) and a test set (30%) in the classification process, with the results of the four immune indices output simultaneously from training and testing, in the model comparison experiment, significantly outperforming the other models. The results of the designed ablation experiment also showed that the proposed multi-stage characteristic distillation structure and attention module were key in improving the accuracy rate. The extensive experiments verify the high efficiency of the proposed method. It is considered the first classification of breast cancer by immunohistochemical results in breast cancer image processing; it provides an effective aid for postoperative breast cancer treatment, greatly reduces the difficulty of diagnosis for doctors, and improves work efficiency.