This study explores the use of deep learning techniques for analyzing lung
Computed Tomography (CT) images. Classic deep learning approaches face
challenges with varying slice counts and resolutions in CT images, a diversity
arising from the utilization of assorted scanning equipment. Typically,
predictions are made on single slices which are then combined for a
comprehensive outcome. Yet, this method does not incorporate learning features
specific to each slice, leading to a compromise in effectiveness. To address
these challenges, we propose an advanced Spatial-Slice Feature Learning
(SSFL++) framework specifically tailored for CT scans. It aims to filter out
out-of-distribution (OOD) data within the entire CT scan, allowing us to select
essential spatial-slice features for analysis by reducing data redundancy by
70\%. Additionally, we introduce a Kernel-Density-based slice Sampling (KDS)
method to enhance stability during training and inference phases, thereby
accelerating convergence and enhancing overall performance. Remarkably, our
experiments reveal that our model achieves promising results with a simple
EfficientNet-2D (E2D) model. The effectiveness of our approach is confirmed on
the COVID-19-CT-DB datasets provided by the DEF-AI-MIA workshop.

该研究探索了使用深度学习技术分析肺部计算机断层扫描（CT）图像的方法。通过提出一种特定于 CT 扫描的先进的空间切片特征学习（SSFL ++）框架和基于核密度的切片采样（KDS）方法，能够选择并分析关键的空间切片特征，降低数据冗余，提高了模型的效果，在 COVID-19-CT-DB 数据集上取得了有希望的结果。