GitHub - Joe1007/E2D: Accepted by Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis workshop (DEF-AI-MIA), in conjunction with CVPR 2024.

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.