Robust COVID-19 Detection in CT Images with CLIP
<|reference_start|>Robust COVID-19 Detection in CT Images with CLIP: In the realm of medical imaging, particularly for COVID-19 detection, deep learning models face substantial challenges such as the necessity for extensive computational resources, the paucity of well-annotated datasets, and a significant amount of unlabeled data. In this work, we introduce the first lightweight detector designed to overcome these obstacles, leveraging a frozen CLIP image encoder and a trainable multilayer perception (MLP). Enhanced with Conditional Value at Risk (CVaR) for robustness and a loss landscape flattening strategy for improved generalization, our model is tailored for high efficacy in COVID-19 detection. Furthermore, we integrate a teacher-student framework to capitalize on the vast amounts of unlabeled data, enabling our model to achieve superior performance despite the inherent data limitations. Experimental results on the COV19-CT-DB dataset demonstrate the effectiveness of our approach, surpassing baseline by up to 10.6% in `macro' F1 score in supervised learning. The code is available at https://github.com/Purdue-M2/COVID-19_Detection_M2_PURDUE.<|reference_end|>
@article{lin2024robust,
    title={Robust COVID-19 Detection in CT Images with CLIP},
    author={Li Lin, Yamini Sri Krubha, Zhenhuan Yang, Cheng Ren, Thuc Duy Le,
  Irene Amerini, Xin Wang, Shu Hu},
    journal={arXiv preprint arXiv:2403.08947},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08947},
    primaryClass={eess.IV cs.CV}
}