Causal Attention-Based Lightweight and Efficient Cervical Cancer Cell Detection Model
Yuanyuan Guo, Dehua Chen, Chengzhuan Bao, Yishu Luo · 2023
Cervical cancer, a prevalent malignancy among women, demands early screening and diagnosis for improved cure rates. Computer-aided screening systems offer accurate and rapid detection, reducing errors and enhancing efficiency. Convolutional neural networks have been extensively employed for cervical cancer cell detection in TCT images. However, these methods often overlook cervical cell morphology and intricate TCT image backgrounds, resulting in biased models. Moreover, their complexity limits practical application efficiency. Nevertheless, the causal attention mechanism can confine the model’s focus solely to the current morphology, preventing interference from other factors and effectively mitigating bias. And, lightweight strategies can enhance model efficiency and performance by reducing parameter count and computational demands. Thus, we introduce a lightweight high-performance model based on causal attention. Our cervical cancer cell detection model integrates causal attention-guided deformable convolutions in its backbone. This novel backbone improves CNN’s capacity to extract cellular morphological features, alleviating bias from target-background correlation. Additionally, a lightweight GSConv structure streamlines the neck architecture, further boosting efficiency. We also introduce a Grad-CAM-based explanation method for swift insight into model predictions. Experimental results demonstrate heightened precision, reducing parameters and computation by 20%, with a 1.9% average mAP increase. Our method reduces complexity and enhances precision in cervical cancer cell detection, showcasing its efficacy and potential in medical image object detection.