MASKED-AP: Attention Pyramid Convolutional Neural Network with Mask for Cervical Cell Classification

The automatic and effective cervical cell classification technique is critical for cervical cytology screening and cervical cancer prevention. We notice that cervical cell classification is a fine-grained classification task. The difference between classes is small while the difference within a class is large, so it is difficult to capture the discriminative features between different classes of cells for classification. To address this problem, this paper proposes an attention pyramid model (Masked-AP) used for cervical cell classification. Our Masked-AP effectively combines high-level semantic features with low-level detailed features extracted from images, which are both important for classification. Further, we also use the attention mask to drive our model to focus more on the cell nuclei containing substantial discriminative information. For model evaluation, we built a cervical cell dataset named LDCC including 17476 images from 507 subjects. LDCC is available at http://ldcc3.biodwhu.cn/LDCC3/. Our method yielded 74.11% accuracy, 74.11% recall, 74.19% precision, and 74.07% F1-score, and outperforms the state-of-the-art methods.