Peng Yao

DMCA-Net: Dual-branch multi-granularity hierarchical contrast and cross-attention network for cervical abnormal cell detection

Accurate detection of abnormal cells is essential for early screening and precise diagnosis of cervical cancer. Despite the recent advances in deep learning-based methods for cervical cancer detection, their broad clinical applications are hindered by several technical challenges. On the one hand, gradually evolved abnormal cells are visually similar to normal cells. On the other hand, single cells and cell clusters exhibit significant appearance variations, overlooking those between normal and abnormal cells. In order to overcome these challenges, we propose a novel dual-branch multi-granularity hierarchical contrast and cross attention network, called DMCA-Net. Specifically, DMCA-Net utilizes dual branches to detect abnormal and normal cells, respectively. Meanwhile, an inter-cell pair-wise cross-attention (IPCA) is utilized to improve feature embedding learning. The IPCA regularizes the attention learning of abnormal cell features by treating normal cell features as distractors. In addition, DMCA-Net also adopts a multi-granularity hierarchical contrastive learning (MHCL) to enhance the classification ability. Our study indicates that MHCL alleviates the interference of intra-class appearance variations in cervical cell, effectively pulls apart the inter-class distance between different classes of cervical cells at different granularities. Extensive experiments on two publicly available datasets demonstrate that our DMCA-Net outperforms existing methods, achieving state-of-the-art (SOTA) results. Code and additional annotation data are available at https://github.com/zhihuaji/DMCA-Net.

SCAC: A Semi-Supervised Learning Approach for Cervical Abnormal Cell Detection

Cervical abnormal cell detection plays a crucial role in the early screening of cervical cancer. In recent years, some deep learning-based methods have been proposed. However, these methods rely heavily on large amounts of annotated images, which are time-consuming and labor-intensive to acquire, thus limiting the detection performance. In this paper, we present a novel Semi-supervised Cervical Abnormal Cell detector (SCAC), which effectively utilizes the abundant unlabeled data. We utilize Transformer as the backbone of SCAC to capture long-range dependencies to mimic the diagnostic process of pathologists. In addition, in SCAC, we design a Unified Strong and Weak Augment strategy (USWA) that unifies two data augmentation pipelines, implementing consistent regularization in semi-supervised learning and enhancing the diversity of the training data. We also develop a Global Attention Feature Pyramid Network (GAFPN), which utilizes the attention mechanism to better extract multi-scale features from cervical cytology images. Notably, we have created an unlabeled cervical cytology image dataset, which can be leveraged by semi-supervised learning to enhance detection accuracy. To the best of our knowledge, this is the first publicly available large unlabeled cervical cytology image dataset. By combining this dataset with two publicly available annotated datasets, we demonstrate that SCAC outperforms other existing methods, achieving state-of-the-art performance. Additionally, comprehensive ablation studies are conducted to validate the effectiveness of USWA and GAFPN. These promising results highlight the capability of SCAC to achieve high diagnostic accuracy and extensive clinical applications.