2000 character limit reached
Robust COVID-19 Detection in CT Images with CLIP (2403.08947v3)
Published 13 Mar 2024 in eess.IV and cs.CV
Abstract: 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.
- A deep learning approach using effective preprocessing techniques to detect covid-19 from chest ct-scan and x-ray images. Computers in biology and medicine, 139:105014, 2021.
- Covid-19 prediction and detection using deep learning. International Journal of Computer Information Systems and Industrial Management Applications, 12:14–14, 2020.
- Data-driven covid-19 detection through medical imaging. In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), page 1–5. IEEE, 2023.
- A large imaging database and novel deep neural architecture for covid-19 diagnosis. In 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), page 1–5. IEEE, 2022.
- Annotation-efficient covid-19 pneumonia lesion segmentation using error-aware unified semisupervised and active learning. IEEE Transactions on Artificial Intelligence, 4(2):255–267, 2022.
- Sharpness-aware minimization for efficiently improving generalization. In International Conference on Learning Representations, 2020.
- Multiple prompt fusion for zero-shot lesion detection using vision-language models. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 283–292. Springer, 2023.
- Distributionally robust survival analysis: A novel fairness loss without demographics. In Machine Learning for Health, pages 62–87. PMLR, 2022.
- Shu Hu et al. Pseudoprop: Robust pseudo-label generation for semi-supervised object detection in autonomous driving systems. In CVPR Workshop, pages 4390–4398, 2022.
- Tkml-ap: Adversarial attacks to top-k multi-label learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7649–7657, 2021.
- Rank-based decomposable losses in machine learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
- Outlier robust adversarial training. In Asian Conference on Machine Learning, pages 454–469. PMLR, 2024.
- Learning by minimizing the sum of ranked range. Advances in Neural Information Processing Systems, 33:21013–21023, 2020.
- Sum of ranked range loss for supervised learning. Journal of Machine Learning Research, 23(112):1–44, 2022.
- Open clip. https://github.com/mlfoundations/open_clip, 2021.
- Improving fairness in deepfake detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 4655–4665, 2024.
- Ai-mia: Covid-19 detection and severity analysis through medical imaging. In European Conference on Computer Vision, page 677–690. Springer, 2022.
- Ai-enabled analysis of 3-d ct scans for diagnosis of covid-19 & its severity. In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), page 1–5. IEEE, 2023.
- A deep neural architecture for harmonizing 3-d input data analysis and decision making in medical imaging. Neurocomputing, 542:126244, 2023.
- Domain adaptation, explainability & fairness in ai for medical image analysis: Diagnosis of covid-19 based on 3-d chest ct-scans. arXiv preprint arXiv:2403.02192, 2024.
- Mia-cov19d: Covid-19 detection through 3-d chest ct image analysis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, page 537–544, 2021.
- Deep transparent prediction through latent representation analysis. arXiv preprint arXiv:2009.07044, 2020.
- Transparent adaptation in deep medical image diagnosis. In TAILOR, page 251–267, 2020.
- Coronavirus disease (covid-19) cases analysis using machine-learning applications. Applied Nanoscience, 13(3):2013–2025, 2023.
- Clip-lung: Textual knowledge-guided lung nodule malignancy prediction. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 403–412. Springer, 2023.
- Large-scale methods for distributionally robust optimization. Advances in Neural Information Processing Systems, 33:8847–8860, 2020.
- Using artificial intelligence to detect covid-19 and community-acquired pneumonia based on pulmonary ct: evaluation of the diagnostic accuracy. Radiology, 296(2):E65–E71, 2020.
- Lung image segmentation based on drd u-net and combined wgan with deep neural network. Computer Methods and Programs in Biomedicine, 226:107097, 2022.
- Preserving fairness generalization in deepfake detection. CVPR, 2024.
- Clip-driven universal model for organ segmentation and tumor detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 21152–21164, 2023.
- Effect of ct-scan image resizing, enhancement and normalization on accuracy of covid-19 detection. In 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA), pages 17–22. IEEE, 2021.
- Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
- Distributionally robust optimization: A review. arXiv preprint arXiv:1908.05659, 2019.
- Optimization of conditional value-at-risk. Journal of risk, 2:21–42, 2000.
- A comprehensive survey of covid-19 detection using medical images. SN Computer Science, 2(6):434, 2021.
- Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for covid-19. IEEE reviews in biomedical engineering, 14:4–15, 2020.
- A review of deep learning-based detection methods for covid-19. Computers in Biology and Medicine, 143:105233, 2022.
- Method and system for diagnosis of covid-19 using artificial intelligence, August 3 2021. US Patent 11,076,824.
- Method and system for diagnosis of covid-19 disease progression using artificial intelligence, June 14 2022. US Patent 11,361,440.
- Method and system for diagnosis of covid-19 using artificial intelligence, June 14 2022. US Patent 11,357,464.
- Medclip: Contrastive learning from unpaired medical images and text. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, 2022.
- Doro: Distributional and outlier robust optimization. In International Conference on Machine Learning, pages 12345–12355. PMLR, 2021.
Sponsor
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.