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Weakly Supervised Global-Local Feature Learning for Cervical Cytology Image Analysis

Existing supervised Convolutional Neural Network (CNN) approaches for cervical cytology image analysis generally rely on the heavy manual annotation for each cell or cell mass and thus lead to extensive time and effort. In this paper, we propose a weakly supervised global-local learning for cervical cytology image analysis. It aims to perform the classification for region of interests (ROIs) and further classify the cells only with the ROI labels. Specifically, the proposed method firstly detects the cells within ROI and extracts the CNN features of cells. Then attention-based bidirectional LSTM (Att-BLSTM) is applied to explore the global contextual information of ROI. On the other hand, the Vision Transformer (ViT) is used to exploit the local attentive representations of the cells in ROIs. The cross attention (CA) is applied to incorporate the global contextual features and local patterns and thus generates more discriminative feature representation of ROI. More importantly, the CA score is used as the pseudo label to select top and least attentive cells. Therefore, the in-the-class and out-of-the-class CA branches are trained to achieve the cell classification. Experimental results demonstrate the effectiveness of our method for cervical cytology ROI and cell classification, and the weak supervision of the image-level label has great potential to promote the automatic whole slide cervical image analysis and alleviate the workload of cytologists.

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