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。

COVID-19 检测中空间切片特征学习的深入研究

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

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。