convolutional neural network; COVID-19 lesion image segmentation; self-attention mechanism; multiscale attention gate; spatial pyramid pooling

Geng P, Tan Z, Wang Y, et al. STCNet: Alternating CNN and improved transformer network for COVID-19 CT image segmentation. Biomedical Signal Processing and Control. 2024; 93: 106205. doi: 10.1016/j.bspc.2024.106205 Niitsu H, Mizumoto M, Li Y, et al. Tumor Response on Diagnostic Imaging after Proton Beam Therapy for Hepatocellular Carcinoma. Cancers. 2024; 16(2): 357. doi: 10.3390/cancers16020357 Shimizu S, Nakai K, Li Y, et al. Boron Neutron Capture Therapy for Recurrent Glioblastoma Multiforme: Imaging Evaluation of a Case with Long-Term Local Control and Survival. Cureus. 2023. doi: 10.7759/cureus.33898 Li S, Mo Y, Li Z. Automated Pneumonia Detection in Chest X-Ray Images Using Deep Learning Model. Innovations in Applied Engineering and Technology. Published online December 12, 2022: 1–6. doi: 10.62836/iaet.vli1.002 Bueno C, Barker MD, Orphan VJ. X-Ray Detector Physics and Applications II. Society of Photo Optical; 1993. doi: 10.1117/12.164737 Zheng T, Lin F, Li X, et al. Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study. eClinicalMedicine. 2023; 58: 101913. doi: 10.1016/j.eclinm.2023.101913 Zhang J, Chen D, Ma D, et al. CdcSegNet: Automatic COVID-19 Infection Segmentation from CT Images. IEEE Transactions on Instrumentation and Measurement. 2023; 72: 1–13. doi: 10.1109/tim.2023.3267355 Shi F, Wang J, Shi J, et al. Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering. 2021; 14: 4–15. doi: 10.1109/rbme.2020.2987975 Milletari F, Navab N, Ahmadi SA. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In: Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV). 2016. doi: 10.1109/3dv.2016.79 Wang G, Liu X, Li C, et al. A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions from CT Images. IEEE Transactions on Medical Imaging. 2020; 39(8): 2653–2663. doi: 10.1109/tmi.2020.3000314 Yu L, Hu Z, Zhang F, et al. Unmanned aerial vehicle image biological soil crust recognition based on UNet++. International Journal of Remote Sensing. 2022; 43(7): 2660–2676. doi: 10.1080/01431161.2022.2066486 Wang B, Jin S, Yan Q, et al. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system. Applied Soft Computing. 2021; 98: 106897. doi: 10.1016/j.asoc.2020.106897 Cong R, Zhang Y, Yang N, et al. Boundary Guided Semantic Learning for Real-Time COVID-19 Lung Infection Segmentation System. IEEE Transactions on Consumer Electronics. 2022; 68(4): 376–386. doi: 10.1109/tce.2022.3205376 Ibtehaz N, Kihara D. Acc-unet: a completely convolutional unet model for the 2020s. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 2023: 692-702. doi: 10.48550/arXiv.2308.13680 Zhou T, Canu S, Ruan S. An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism. ARXIV preprint arXiv:2004.06673, 2020. Li CF, Xu YD, Ding XH, et al. MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification. Computers in Biology and Medicine. 2022; 144: 105340. doi: 10.1016/j.compbiomed.2022.105340 Xiao H, Ran Z, Mabu S, et al. SAUNet++: an automatic segmentation model of COVID-19 lesion from CT slices. The Visual Computer. 2022; 39(6): 2291–2304. doi: 10.1007/s00371-022-02414-4 Zhao S, Li Z, Chen Y, et al. SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images. Pattern Recognition. 2021; 119: 108109. doi: 10.1016/j.patcog.2021.108109 Jia W, Ma S, Geng P, et al. DT-Net: Joint Dual-Input Transformer and CNN for Retinal Vessel Segmentation. Computers, Materials & Continua. 2023; 76(3): 3393–3411. doi: 10.32604/cmc.2023.040091 Karlinsky L, Michaeli T, Nishino K, et al. Computer Vision – ECCV 2022 Workshops. Springer Nature Switzerland; 2023. doi: 10.1007/978-3-031-25066-8 Li K, Wang Y, Gao P, et al. Uniformer: unified transformer for efficient spatiotemporal representation learning. ARXIV preprint arXiv:2201.04676. 2022. Bello IM, Zhang K, Su Y, et al. Densely multiscale framework for segmentation of high resolution remote sensing imagery. Computers & Geosciences. 2022; 167: 105196. doi: 10.1016/j.cageo.2022.105196 Azad R, Heidari M, Shariatnia M, et al. Transdeeplab: convolution-free transformer-based deeplab v3+ for medical image segmentation. Proceeding of the International Workshop on Predictive Intelligence in Medicine. 2022: 91-102. doi: 10.48550/arXiv.2208.00713 Tang F, Wang L, Ning C, et al. CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network. 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). 2023. doi: 10.1109/isbi53787.2023.10230609 Zhang K, Liu X, Shen J, et al. Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography. Cell. 2020; 181(6): 1423–1433.e11. doi: 10.1016/j.cell.2020.04.045 Ma J, Wang Y, An X, et al. Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation. Medical Physics. 2021; 48(3): 1197–1210. doi: 10.1002/mp.14676 Liu J, Zhao D, Shen J, et al. HRD-Net: High resolution segmentation network with adaptive learning ability of retinal vessel features. Computers in Biology and Medicine. 2024; 173: 108295. doi: 10.1016/j.compbiomed.2024.108295 Geng P, Lu J, Zhang Y, et al. TC-Fuse: A Transformers Fusing CNNs Network for Medical Image Segmentation. Computer Modeling in Engineering & Sciences. 2023; 137(2): 2001–2023. doi: 10.32604/cmes.2023.027127 Chen L, Bentley P, Mori K, et al. DRINet for Medical Image Segmentation. IEEE Transactions on Medical Imaging. 2018; 37(11): 2453–2462. doi: 10.1109/tmi.2018.2835303 Wang R, Lei T, Cui R, et al. Medical image segmentation using deep learning: A survey. IET Image Processing. 2022; 16(5): 1243–1267. doi: 10.1049/ipr2.12419 Fan DP, Zhou T, Ji GP, et al. Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images. IEEE Transactions on Medical Imaging. 2020; 39(8): 2626–2637. doi: 10.1109/tmi.2020.2996645 Cao H, Wang Y, Chen J, et al. Swin-unet: unet-like pure transformer for medical image segmentation. In: Proceedings of the European Conference on Computer Vision; 2022. doi: 10.48550/arXiv.2105.05537