Robust COVID-19 Detection in CT Images with CLIP
arxiv(2024)
摘要
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
learning. The code is available at
https://github.com/Purdue-M2/COVID-19_Detection_M2_PURDUE.
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