Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling

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.

@article{lee2025_2507.01564,
  title={ Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling },
  author={ Chia-Ming Lee and Bo-Cheng Qiu and Ting-Yao Chen and Ming-Han Sun and Fang-Ying Lin and Jung-Tse Tsai and I-An Tsai and Yu-Fan Lin and Chih-Chung Hsu },
  journal={arXiv preprint arXiv:2507.01564},
  year={ 2025 }
}