用可切换卷积与特征金字塔提升显微图像病毒检测精度
Detection of Virus and Small Cell Patches in Foci Images Using Switchable Convolution and Feature Pyramid Networks
- 引入可切换空洞卷积适应不同尺度目标，增强感受野灵活性
- 小细胞斑点检测mAP达40.5%（IoU=25%），病毒斑点检测mAP达68%
- 适合生物医学显微图像中微小、密集目标的精准定位任务
准确检测和计数焦点形成单位（FFU）图像中的病毒斑点对量化病毒感染程度和分析细胞结构至关重要。该任务具有挑战性，因为生物医学目标在大小、密度、对比度和形状上差异显著。本文提出一种基于YOLOv2的改进检测器，融合特征金字塔网络（FPN）以增强多尺度特征表示，并引入可切换空洞卷积机制，适应密集显微图像中细粒度目标的感受野需求。所提方法在生物医学焦点图像数据集上进行评估，用于病毒斑点和小细胞斑点检测。对于小细胞斑点检测，模型在25%交并比（IoU）阈值下达到40.5%的平均精度均值（mAP）；对于FFU病毒斑点检测，达到68%的mAP。结果表明，结合基于FPN的特征融合与可切换卷积，显著提升了YOLOv2在特定生物医学目标检测任务中的适用性。
原文摘要 · Abstract (English)
Accurate detection and counting of virus patches in focus-forming unit (FFU) images, also known as foci images, are important for quantifying viral infection and analyzing cellular structures. This task is challenging because biomedical targets often vary substantially in size, density, contrast, and shape. In this paper, we propose an enhanced YOLOv2-based detector that integrates a Feature Pyramid Network (FPN) to improve multi-scale feature representation. We also incorporate a switchable atrous convolution mechanism to adapt the receptive field for fine-grained targets in dense microscopy images. The proposed method is evaluated on biomedical foci image datasets for virus patch and small cell patch detection. For small cell patch detection, the model achieves a mean average precision (mAP) of 40.5% at a 25% Intersection over Union (IoU) threshold. For FFU virus patch detection, the model achieves an mAP of 68%. These results indicate that combining FPN-based feature fusion with switchable convolution improves the suitability of YOLOv2 for specialized biomedical object detection tasks
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