Feature extraction from histopathological images based on nucleus-guided convolutional neural network for breast lesion classification

Feature extraction is a crucial and challenging aspect in the computer-aided diagnosis of breast cancer with histopathological images. In recent years, many machine learning methods have been introduced to extract features from histopathological images. In this study, a novel nucleus-guided feature extraction framework based on convolutional neural network is proposed for histopathological images. The nuclei are first detected from images, and then used to train a designed convolutional neural network with three hierarchy structures. Through the trained network, image-level features including the pattern and spatial distribution of the nuclei are extracted. The proposed features are evaluated through the classification experiment on a histopathological image database of breast lesions. The experimental results show that the extracted features effectively represent histopathological images, and the proposed framework achieves a better classification performance for breast lesions than the compared state-of-the-art methods.