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

Chih-Chung Hsu, Chia-Ming Lee, Yang Fan Chiang, Yi-Shiuan Chou, Chih-Yu Jiang, Shen-Chieh Tai, Chi-Han Tsai; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4924-4934

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 aims to filter out OOD data within the entire CT scan enabling us to select crucial spatial slices for analysis by reducing 70% redundancy totally. Meanwhile we proposed Kernel-Density-based slice Sampling (KDS) method to improve the stability during 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 code is available at https://github.com/ming053l/E2D.