A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection

CVPR 2024

arXiv Code technical report
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The brief illustration for SSFL++. It aim to reduce redundancy in spatial and slice dimension on whole CT-scan to improve model and data quality. (1) Left: original CT-scan. (2) Middle: after reduction at spatial. (3) Right: after reduction at slices.
Abstract
Conventional Computed Tomography (CT) imaging recognition faces two significant challenges: (1) There is often considerable variability in the resolution and size of each CT scan, necessitating strict requirements for the input size and adaptability of models. (2) CT-scan contains large number of out-of-distribution (OOD) slices. The crucial features may only be present in specific spatial regions and slices of the entire CT scan. How can we effectively figure out where these are located? To deal with this, we introduce an enhanced Spatial-Slice Feature Learning (SSFL++) framework specifically designed for CT scan. It aim to filter out a OOD data within whole CT scan, enabling our to select crucial spatial-slice for analysis by reducing 70% redundancy totally. Meanwhile, we proposed Kernel-Density-based slice Sampling (KDS) method to improve the stability when training and inference stage, therefore speeding up the rate of convergence and boosting performance. As a result, the experiments demonstrate the promising performance of our model using a simple EfficientNet-2D (E2D) model, even with only 1% of the training data. The efficacy of our approach has been validated on the COVID-19-CT-DB datasets provided by the DEF-AI-MIA workshop, in conjunction with CVPR 2024.
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The GradCAM++ visualization before and after proposed SSFL++. By reducing redundancy on the spatial scale, we can implicitly enhance the visual effectiveness of Explainable AI, thereby facilitating clinical applications.
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The reduction in redundant data achieved by the SSFL++ module is evaluated across three dimensions: spatial, slice, and overall.This approach quantifies the efficiency of the SSFL++ module in reducing unnecessary information in CT scans, enabling more focused analysis and processing. By minimizing data redundancy, the module enhances computational efficiency and potentially improves the accuracy of subsequent analyses or models applied to the CT data.
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The comparison between random sampling, systematic sampling, and the proposed KDS method is noteworthy. As illustrated, random sampling fails to uniformly sample CT slices of varying area sizes, tending to select larger areas while neglecting global information. This results in greater bias and randomness during training and inference. On the other hand, systematic sampling divides the area into equally lengthened sub-intervals before randomly selecting samples from them. Although this approach can capture global information, it is ineffective at sampling the most crucial CT slices. Our proposed KDS method combines the advantages of both methods without their drawbacks, achieving a better balance. KDS can implicitly improve data efficiency, thereby enhancing the model’s few-shot capability.
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CT slices from different views and body parts, as well as the results after processing through the spatial step in our proposed SSFL++, are presented. From left to right, the sequence represents the process of CT imaging, where OOD data tend to concentrate at the beginning and the end. The middle section represents the RoI area. As shown in the figure, SSFL++ performs well under various conditions.
If you find our work helpful, please consider citing the following:

BibTeX

 
        @misc{hsu2024closer,
              title={A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection}, 
              author={Chih-Chung Hsu and Chia-Ming Lee and Yang Fan Chiang and Yi-Shiuan Chou and Chih-Yu Jiang and Shen-Chieh Tai and Chi-Han Tsai},
              year={2024},
              eprint={2404.01643},
              archivePrefix={arXiv},
              primaryClass={eess.IV}

        @misc{hsu2024simple,
              title={Simple 2D Convolutional Neural Network-based Approach for COVID-19 Detection}, 
              author={Chih-Chung Hsu and Chia-Ming Lee and Yang Fan Chiang and Yi-Shiuan Chou and Chih-Yu Jiang and Shen-Chieh Tai and Chi-Han Tsai},
              year={2024},
              eprint={2403.11230},
              archivePrefix={arXiv},
              primaryClass={eess.IV}
}