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| 基于深度学习的双期相CT肝癌检测算法 |
| Deep Learning-Based Dual-Phase CT Hepatocellular Carcinoma Detection Algorithm |
| 投稿时间：2024-01-23 修订日期：2024-03-30 |
| DOI： |
| 中文关键词: 深度学习 目标检测 医学影像 注意力机制 |
| 英文关键词:deep learning object detection medical imaging attention mechanism |
| 基金项目:2022年政府资助临床医学优秀人才培养项目 (项目编号：冀财预复[2022]180号) |
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| 中文摘要: |
| 肝癌是一种恶性肿瘤，对其进行早期筛查和准确检测是提高治疗效果、延长患者生存期的关键。针对使用单期相计算机断层扫描（Computed Tomography, CT）图像难以准确检测复杂多变的肝癌的问题，本文提出一种基于FCOS（Fully Convolutional One-Stage Object Detection）的双期相CT肝癌检测方法。首先构建了双期相肝脏CT四元组网络，并利用其匹配双期相肝脏CT切片，确保不同期相之间肝脏位置的一致性，为后续肝癌检测奠定基础。其次改造了FCOS网络以接收双期相CT图像的输入，设计并插入AFF（Attention-based Feature Fusion）模块进行带混合注意力的特征融合，以提高肝癌检测的准确性。实验结果表明，改进算法在本文数据集上的AP达到了78.56%，相比于单期相FCOS网络提高了4.9%，展现出更优越的性能。 |
| 英文摘要: |
| Liver cancer is a kind of malignant tumor, early screening and accurate detection is the key to improve the treatment effect and prolong the survival of patients. In view of the difficulty of accurately detecting complex and variable liver cancer using single-phase Computed Tomography (CT) images in this paper, a dual-phase CT method for liver cancer Detection based on FCOS (Fully Convolutional One-Stage Object Detection) is proposed. Firstly, the dual-phase liver CT quadtuple network is constructed and used to match the dual-phase liver CT sections to ensure the consistency of liver position between different phases and lay a foundation for the subsequent detection of liver cancer. Secondly, FCOS network is improved to receive input of dual-phase CT images, AFF (Attention-based Feature Fusion) module is designed and inserted, and feature fusion with mixed attention is performed at the same time to improve the accuracy of liver cancer detection. The experimental results show that the AP of the improved algorithm on the data set reaches 78.56%, which is 4.9% higher than that of the single-phase FCOS network, and shows better performance. |
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