Computer Engineering, Volume. 51, Issue 8, 373(2025)
Attention Distillation Contrastive Mutual Learning Model for COVID-19 Image Diagnosis
[3] [3] JAISWAL A, GIANCHANDANI N, SINGH D, et al. Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning[J]. Journal of Biomolecular Structure and Dynamics, 2021, 39(15): 5682-5689.
[4] [4] SEN S, SAHA S, CHATTERJEE S, et al. A bi-stage feature selection approach for COVID-19 prediction using chest CT images[J]. Applied Intelligence, 2021, 51(12): 8985-9000.
[5] [5] GAO K, SU J, JIANG Z, et al. Dual-branch combination network: towards accurate diagnosis and lesion segmentation of COVID-19 using CT images[J]. Medical Image Analysis, 2021, 67: 101836.
[6] [6] PANWAR H, GUPTA P K, SIDDIQUI M K, et al. A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-scan images[J]. Chaos, Solitons & Fractals, 2020, 140: 110190.
[8] [8] WANG L, LIN Z Q, WONG A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images[J]. Scientific Reports, 2020, 10(1): 19549.
[9] [9] GAUR P, MALAVIYA V, GUPTA A, et al. COVID-19 disease identification from chest CT images using empirical wavelet transformation and transfer learning[J]. Biomedical Signal Processing and Control, 2022, 71: 103076.
[10] [10] WANG Z, LIU Q, DOU Q. Contrastive cross-site learning with redesigned net for COVID-19 CT classification[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 24(10): 2806-2813.
[11] [11] LU F, ZHANG Z, ZHAO S, et al. CMM: a CNN-MLP model for COVID-19 lesion segmentation and severity grading[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024, 21(4): 789-802.
[12] [12] RAHIMZADEH M, ATTAR A. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2[J]. Informatics in Medicine Unlocked, 2020, 19: 100360.
[13] [13] LEBEDEV V, LEMPITSKY V. Fast ConvNets using group-wise brain damage[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE Press, 2016: 512-521.
[14] [14] ZOPH B, VASUDEVAN V, SHLENS J, et al. Learning transferable architectures for scalable image recognition[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE Press, 2018: 442-451.
[15] [15] GARG M, DHIMAN G. A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants[J]. Neural Computing and Applications, 2021, 33(4): 1311-1328.
[16] [16] TAN M, LE Q. Efficientnet: rethinking model scaling for convolutional neural networks[C]//Proceedings of IEEE International Conference on Machine Learning. Washington D.C., USA: IEEE Press, 2019.
[17] [17] LIM S, KIM I, KIM T, et al. Fast autoaugment[C]//Proceedings of Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2019: 226-237.
[19] [19] CHEN L F, WANG K L, LI M, et al. K-means clustering-based kernel canonical correlation analysis for multimodal emotion recognition in human-robot interaction[J]. IEEE Transactions on Industrial Electronics, 2022, 70(1): 1016-1024.
[20] [20] PROKHORENKOVA L, GUSEV G, VOROBEV A, et al. CatBoost: unbiased boosting with categorical features[EB/OL]. [2023-11-10]. https://arxiv.org/abs/1706.09516v5.
[21] [21] HADSELL R, CHOPRA S, LECUN Y. Dimensionality reduction by learning an invariant mapping[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: ACM Press, 2006: 159-167.
[22] [22] YANG X Y, HE X H, ZHAO J Y, et al. COVID-CT-dataset: a CT scan dataset about COVID-19[EB/OL]. [2023-11-10]. https://arxiv.org/abs/2003.13865v3.
[23] [23] SOARES E, ANGELOV P, BIASO S, et al. SARS-CoV-2 CT-scan dataset: a large dataset of real patients CT scans for SARS-CoV-2 identification[EB/OL]. [2023-11-10]. https://arxiv.org/abs/2007.15842.
[24] [24] RAHMAN T, KHANDAKAR A, QIBLAWEY Y, et al. Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images[J]. Computers in Biology and Medicine, 2021, 132: 104319.
[25] [25] HUANG L, RUAN S, DENOEUX T. Covid-19 classification with deep neural network and belief functions[C]//Proceedings of the 5th International Conference on Biological Information and Biomedical Engineering. New York, USA: ACM Press, 2021: 224-231.
[26] [26] LIU Q, DOU Q, YU L, et al. MS-net: multi-site network for improving prostate segmentation with heterogeneous MRI data[J]. IEEE Transactions on Medical Imaging, 2020, 39(9): 2713-2724.
[27] [27] ZHANG H B, LIANG W N, LI C X, et al. DCML: Deep contrastive mutual learning for COVID-19 recognition[J]. Biomedical Signal Processing and Control, 2022, 77: 103770.
[28] [28] YANG H, WANG L Y, XU Y T, et al. CovidViT: a novel neural network with self-attention mechanism to detect Covid-19 through X-ray images[J]. International Journal of Machine Learning and Cybernetics, 2023, 14(3): 973-987.
Get Citation
Copy Citation Text
LÜ, HU Lang, LIANG Weinan, LI Guangli, ZHANG Hongbin. Attention Distillation Contrastive Mutual Learning Model for COVID-19 Image Diagnosis[J]. Computer Engineering, 2025, 51(8): 373
Category:
Received: Dec. 14, 2023
Accepted: Aug. 26, 2025
Published Online: Aug. 26, 2025
The Author Email: LÜ (jingqinlv@ecjtu.edu.cn)