MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images

References

• Zu ZY, Jiang MD, Xu PP, et al. Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology. 2020;296(2):E15–E25. doi:10.1148/radiol.2020200490
   PubMed Web of Science ®Google Scholar
• Asgari Taghanaki S, Abhishek K, Cohen J, et al. Deep semantic segmentation of natural and medical images: a review. Artif Intell Rev. 2021;54(1):137–178. doi:10.1007/s10462-020-09854-1
   Web of Science ®Google Scholar
• Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Cham: Springer; 2015: 234–241.
   Google Scholar
• Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, et al.. Unet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Cham: Springer; 2018:3–11.
   Google Scholar
• Huang H, Lin L, Tong R, et al. Unet 3+: a full-scale connected unet for medical image segmentation. ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2020: 1055–1059.
   Google Scholar
• Oktay O, Schlemper J, Folgoc LL, et al.. Attention u-net: learning where to look for the pancrea. arXiv preprint arXiv:1804.03999; 2018.
   Google Scholar
• Jha D, Smedsrud PH, Riegler MA, et al. Resunet++: an advanced architecture for medical image segmentation. 2019 IEEE International Symposium on Multimedia (ISM). IEEE; 2019: 225–2255.
   Google Scholar
• Lou A, Guan S, Loew M. DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation[C]//Medical Imaging 2021: image Processing. SPIE. 2021;11596:758–768.
   Google Scholar
• Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition; 2015: 3431–3440.
   Google Scholar
• Dosovitskiy A, Beyer L, Kolesnikov A, et al.. An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929; 2020.
   Google Scholar
• Liu Z, Lin Y, Cao Y, et al. Swin transformer: hierarchical vision transformer using shifted window. Proceedings of the IEEE/CVF International Conference on Computer Vision; 2021: 10012–10022.
   Google Scholar
• Chen J, Lu Y, Yu Q, et al.. Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306; 2021.
   Google Scholar
• Cao H, Wang Y, Chen J, et al.. Swin-unet: unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:2105.05537; 2021.
   Google Scholar
• Fan CM, Liu TJ, Liu KH. SUNet: swin transformer UNet for image denoising. arXiv preprint arXiv:2202.14009; 2022.
   Google Scholar
• Tolstikhin IO, Houlsby N, Kolesnikov A, et al. Mlp-mixer: an all-mlp architecture for vision. Adv Neural Inf Process Syst. 2021;34:24261–24272.
   Google Scholar
• Lian D, Yu Z, Sun X, et al.. As-mlp: an axial shifted mlp architecture for vision. arXiv preprint arXiv:2107.08391; 2021.
   Google Scholar
• Masood S, Sharif M, Masood A, et al. A survey on medical image segmentation. Curr Med Imaging. 2015;11(1):3–14. doi:10.2174/157340561101150423103441
   Web of Science ®Google Scholar
• Minaee S, Boykov YY, Porikli F, et al. Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Mach Intell. 2021;44(7):3523–3542.
   Web of Science ®Google Scholar
• Gu R, Wang G, Song T, et al. CA-Net: comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Trans Med Imaging. 2020;40(2):699–711. doi:10.1109/TMI.2020.3035253
   Web of Science ®Google Scholar
• Han Z, Jian M, Wang GG. ConvUNeXt: an efficient convolution neural network for medical image segmentation. Knowl Based Syst. 2022;253:109512. doi:10.1016/j.knosys.2022.109512
   Web of Science ®Google Scholar
• Dai D, Dong C, Xu S, et al. Ms RED: a novel multi-scale residual encoding and decoding network for skin lesion segmentation. Med Image Anal. 2022;75:102293. doi:10.1016/j.media.2021.102293
   PubMed Web of Science ®Google Scholar
• Valanarasu JMJ, Patel VM. UNeXt: MLP-based Rapid Medical Image Segmentation Network. arXiv preprint arXiv:2203.04967; 2022.
   Google Scholar
• Fan DP, Zhou T, Ji GP, et al. Inf-net: automatic covid-19 lung infection segmentation from ct images. IEEE Trans Med Imaging. 2020;39(8):2626–2637. doi:10.1109/TMI.2020.2996645
   PubMed Web of Science ®Google Scholar
• Wang G, Liu X, Li C, et al. A noise-robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images. IEEE Trans Med Imaging. 2020;39(8):2653–2663. doi:10.1109/TMI.2020.3000314
   PubMed Web of Science ®Google Scholar
• Zhou L, Li Z, Zhou J, et al. A rapid, accurate and machine-agnostic segmentation and quantification method for CT-based COVID-19 diagnosis. IEEE Trans Med Imaging. 2020;39(8):2638–2652. doi:10.1109/TMI.2020.3001810
   PubMed Web of Science ®Google Scholar
• Qiu Y, Liu Y, Li S, et al. Miniseg: an extremely minimum network for efficient covid-19. Proc AAAI Conf Artif Intell. 2021;35(6):4846–4854. doi:10.1609/aaai.v35i6.16617
   Google Scholar
• Karthik R, Menaka R, Hariharan M, et al. Contour-enhanced attention CNN for CT-based COVID-19 segmentation. Pattern Recognit. 2022;125:108538. doi:10.1016/j.patcog.2022.108538
   PubMed Web of Science ®Google Scholar
• Chowdhury MEH, Rahman T, Khandakar A, et al. Can AI help in screening viral and COVID-19 pneumonia?. IEEE Access. 2020;8:132665–132676. doi:10.1109/ACCESS.2020.3010287
   Web of Science ®Google Scholar
• Rahman T, Khandakar A, Qiblawey Y, et al. Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Comput Biol Med. 2021;132:104319. doi:10.1016/j.compbiomed.2021.104319
   PubMed Web of Science ®Google Scholar
• Tahir AM, Chowdhury MEH, Khandakar A, et al. COVID-19 infection localization and severity grading from chest X-ray images. Comput Biol Med. 2021;139:105002. doi:10.1016/j.compbiomed.2021.105002
   PubMed Web of Science ®Google Scholar
• Degerli A, Ahishali M, Yamac M, et al. COVID-19 infection map generation and detection from chest X-ray images. Health Inf Sci Syst. 2021;9(1):1–16. doi:10.1007/s13755-021-00146-8
   PubMed Web of Science ®Google Scholar
• Codella NCF, Gutman D, Celebi ME, et al. Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (ISIC). 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE; 2018: 168–172.
   Google Scholar
• Xu Q, Duan W, He N. DCSAU-net: a deeper and more compact split-attention U-Net for medical image segmentation. arXiv preprint arXiv:2202.00972; 2022.
   Google Scholar
• Trebing K, Staǹczyk T, Mehrkanoon S. SmaAt-UNet: precipitation nowcasting using a small attention-UNet architecture. Pattern Recognit Lett. 2021;145:178–186. doi:10.1016/j.patrec.2021.01.036
   Web of Science ®Google Scholar
• Selvaraju RR, Cogswell M, Das A, et al. Grad-cam: visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE international conference on computer vision; 2017: 618–626.
   Google Scholar