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.
Our source code will be made available.

利用 Spatial-Slice 特征学习 (SSFL++) 框架和 Kernel-Density-based 切片采样 (KDS) 方法，专门针对 CT 扫描，提高模型的适应性和稳定性，通过减少冗余提取关键空间切片进行分析，实验结果表明即使只使用 1％的训练数据，也能够使用简单的 EfficientNet-2D (E2D) 模型取得良好的性能，该方法在提供的 COVID-19-CT-DB 数据集上进行了验证。