基于核密度采样，从多中心肺部CT中精准提取代表性切片
Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling
- 用核密度方法自适应选取每例扫描的8张代表性切片
- EfficientNet模型在多中心数据上达94.68% F1分数
- 适合医疗影像跨机构分析与数据均衡性研究
我们针对多源新冠肺炎检测挑战提出解决方案，对来自四个不同医疗中心的胸部CT扫描进行分类。为应对多源异质性，采用基于空间切片特征学习（SSFL）框架与核密度切片采样（KDS）方法。预处理流程包括肺区提取、质量控制和自适应切片采样，每例扫描选取8张代表性切片。在验证集上对比EfficientNet与Swin Transformer架构，EfficientNet取得94.68% F1分数，优于Swin Transformer的93.34%。结果表明，基于KDS的流程在多源数据上有效，凸显多机构医学影像评估中数据平衡的重要性。
原文摘要 · Abstract (English)
We present our solution for the Multi-Source COVID-19 Detection Challenge, which classifies chest CT scans from four distinct medical centers. To address multi-source variability, we employ the Spatial-Slice Feature Learning (SSFL) framework with Kernel-Density-based Slice Sampling (KDS). Our preprocessing pipeline combines lung region extraction, quality control, and adaptive slice sampling to select eight representative slices per scan. We compare EfficientNet and Swin Transformer architectures on the validation set. The EfficientNet model achieves an F1-score of 94.68%, compared to the Swin Transformer's 93.34%. The results demonstrate the effectiveness of our KDS-based pipeline on multi-source data and highlight the importance of dataset balance in multi-institutional medical imaging evaluation.
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