Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling
Journal:
arXiv
Published Date:
Jul 2, 2025
Abstract
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.
