Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911052494536704 |
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| author | Lee, Chia-Ming Qiu, Bo-Cheng Chen, Ting-Yao Sun, Ming-Han Lin, Fang-Ying Tsai, Jung-Tse Tsai, I-An Lin, Yu-Fan Hsu, Chih-Chung |
| author_facet | Lee, Chia-Ming Qiu, Bo-Cheng Chen, Ting-Yao Sun, Ming-Han Lin, Fang-Ying Tsai, Jung-Tse Tsai, I-An Lin, Yu-Fan Hsu, Chih-Chung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01564 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling Lee, Chia-Ming Qiu, Bo-Cheng Chen, Ting-Yao Sun, Ming-Han Lin, Fang-Ying Tsai, Jung-Tse Tsai, I-An Lin, Yu-Fan Hsu, Chih-Chung Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.01564 |