Fetching the paper…
Reading the bibliography…
This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022).
2017
Earlier work this paper cites.
Kollias, D., Tagaris, A., Stafylopatis, A., Kollias, S., Tagaris, G.: Deep neural architectures for prediction in healthcare. Complex & Intelligent Systems 4
2018
Earlier work this paper cites.
Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., et al.: Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Scientific Reports 10
2020
Earlier work this paper cites.
Jin, S., Wang, B., Xu, H., Luo, C., Wei, L., Zhao, W., et al.: Ai-assisted ct imaging analysis for covid-19 screening: building and deploying a medical ai system in four weeks. MedRxiv (2020)
2020
Earlier work this paper cites.
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised contrastive learning. In: Annual Conference on Neural Information Processing Systems 2020 (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Kollias, D., Vlaxos, Y., Seferis, M., Kollia, I., Sukissian, L., Wingate, J., Kollias, S.D.: Transparent adaptation in deep medical image diagnosis. In: TAILOR. pp. 251–267 (2020)
2020
Cited alongside, same era.
Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., et al.: Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology 296
2020
Cited alongside, same era.
Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., et al.: Deep learning enables accurate diagnosis of novel coronavirus (covid-19) with ct images. MedRxiv (2020)
2020
Cited alongside, same era.
Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., et al.: A weakly-supervised framework for covid-19 classification and lesion localization from chest ct. IEEE Transactions on Medical Imaging 39
2020
Cited alongside, same era.
Hou, J., Xu, J., Feng, R., Zhang, Y., Shan, F., Shi, W.: Cmc-cov19d: Contrastive mixup classification for covid-19 diagnosis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 454–461 (2021)
2021
Later among the works it cites.
Hou, J., Xu, J., Jiang, L., Du, S., Feng, R., Zhang, Y., Shan, F., Xue, X.: Periphery-aware covid-19 diagnosis with contrastive representation enhancement. Pattern Recognition 118
2021
Later among the works it cites.
2021
Later among the works it cites.
Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., et al.: A deep learning algorithm using ct images to screen for corona virus disease (covid-19). European radiology pp. 1–9 (2021)
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wang, Z., Xiao, Y., Li, Y., Zhang, J., Lu, F., Hou, M., et al.: Automatically discriminating and localizing covid-19 from community-acquired pneumonia on chest x-rays. Pattern Recognition 110
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2022
Closest in time.
WHO: Coronavirus disease (covid-19) pandemic. https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (2022)
2022
Closest in time.