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

SSFL++, E2D [Paper Link] [Project Page] [Technical Report]

Chih-Chung Hsu, Chia-Ming Lee, Yang Fan Chiang, Yi-Shiuan Chou, Chih-Yu Jiang, Sheng-Chieh Tai, Chi-Han Tsai

Advanced Computer Vision LAB, National Cheng Kung University

Overview

  • SSFL++: Remove redundancy on spatial/slice dimension with kernel-density-aware slice sampling for CT scan. (removing 70% reduncancy and with global sequantial modeling)

  • E2D: Simple 2D-CNN for COVID-19 Detection. (few-shot aware, robust, and efficient)

  • Generalizability: A Unified redundancy removal framework for CT-scan-like data.

Data Reduction Overview

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.

Spatial Area (K) Slice Length Spatial × Slice (M)
Before After Δ (%) Before After Δ (%) Before After Δ (%)
Training 267.25 155.53 0.4184 285.32 142.91 0.4983 76.25 22.22 0.7085
Positive 266.42 157.69 0.4088 295.90 148.18 0.4985 78.83 23.36 0.7036
Negative 268.21 153.03 0.4296 273.97 137.26 0.4981 73.48 21.00 0.7141
Validation 265.62 155.23 0.4172 281.95 141.23 0.4984 74.89 21.92 0.7072
Positive 268.94 160.48 0.4061 280.53 140.55 0.4984 75.45 22.55 0.7010
Negative 262.12 149.69 0.4288 283.49 141.97 0.4984 74.30 21.25 0.7139
(T+V) Positive 267.25 155.53 0.4184 292.96 146.72 0.4985 78.29 22.81 0.7085
(T+V) Negative 267.01 152.37 0.4294 275.78 138.16 0.4982 73.64 21.05 0.7141
Total 266.94 155.47 0.4182 284.68 142.59 0.4983 75.99 22.16 0.7082
Testing 279.55 153.41 0.4520 309.39 154.67 0.5003 86.48 23.72 0.7256

Table: The reduction metrics by the SSFL++ module across training/valid/testing set.

Environment

Installation

git clone https://github.com/ming053l/E2D.git
conda create --name e2d python=3.8 -y
conda activate e2d
# CUDA 11.6
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge

How To Test

  • Run ./preproceesing/inference/* step by step and then (you need to change directory within all file.)
CUDA_VISIBLE_DEVICES=0,1 python inference.py

How To Train

  • Run ./preproceesing/* step by step and then (you need to change directory within all file.)
CUDA_VISIBLE_DEVICES=0,1 python train.py

Citations

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}
}
@INPROCEEDINGS{10192945,
    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},
    booktitle={2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)}, 
    title={Bag of Tricks of Hybrid Network for Covid-19 Detection of CT Scans}, 
    year={2023},
    pages={1-4}
}
@InProceedings{Hsu_2024_CVPR,
    author    = {Hsu, Chih-Chung and Lee, Chia-Ming and Chiang, Yang Fan and Chou, Yi-Shiuan and Jiang, Chih-Yu and Tai, Shen-Chieh and Tsai, Chi-Han},
    title     = {A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2024},
    pages     = {4924-4934}
}

Contact

If you have any question, please email zuw408421476@gmail.com to discuss with the author.