Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (18): 239-247.DOI: 10.3778/j.issn.1002-8331.2306-0160
• Graphics and Image Processing • Previous Articles Next Articles
LIAO Wentao, XU Guoping, WU Xinglong, ZHANG Xuan, ZHOU Huabing
Online:
2024-09-15
Published:
2024-09-13
廖文涛,徐国平,吴兴隆,张炫,周华兵
LIAO Wentao, XU Guoping, WU Xinglong, ZHANG Xuan, ZHOU Huabing. Integration of Domain Adaptation Network and Multiscale Feature Aggregation for Polyp Segmentation[J]. Computer Engineering and Applications, 2024, 60(18): 239-247.
廖文涛, 徐国平, 吴兴隆, 张炫, 周华兵. 融合域自适应网络和多尺度特征聚合的息肉分割网络[J]. 计算机工程与应用, 2024, 60(18): 239-247.
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[1] 张欢, 刘静, 冯毅博, 等. U-Net及其在肝脏和肝脏肿瘤分割中的应用综述[J]. 计算机工程与应用, 2022, 58(2): 1-14. ZHANG H, LIU J, FENG Y B, et al. Review of U-Net and its application in segmentation of liver and liver tumors[J]. Computer Engineering and Applications, 2022, 58(2): 1-14. [2] 张正杰, 程云章, 黄陈. 影像组学在结直肠癌诊疗中的应用及研究进展[J]. 生物医学工程研究, 2023, 42(1): 96-99. ZHANG Z J , CHENG Y H , HUANG C. Application and research progress of radiomics in the diagnosis and treatment of colorectal cancer[J]. Journal of Biomedical Engineering Research, 2023, 42(1): 96-99. [3] 田传鑫, 赵磊. 结直肠癌及结直肠癌肝转移流行病学特点[J]. 中华肿瘤防治杂志, 2021, 28(13): 1033-1038. TIAN C X, ZHAO L. Epidemiological characteristics of colorectal cancer and colorectal liver metastasis[J].Chinese Journal of Cancer Prevention and Treatment, 2021, 28(13): 1033-1038. [4] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//Proceeding of 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2015), Munich, Germany, October 5-9, 2015: 234-241. [5] FANG Y , CHEN C , YUAN Y , et al. Selective feature aggregation network with area-boundary constraints for polyp segmentation[C]//Proceedings of 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), Shenzhen, China, October 13-17, 2019: 302-310. [6] FAN D P, JI G P, ZHOU T, et al. PraNet: parallel reverse attention network for polyp segmentation[C]//Proceedings of 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Lima, Peru, October 4-8, 2020: 263-273. [7] KIM T, LEE H, KIM D. UACANet: uncertainty augmented context attention for polyp segmentation[C]//Proceedings of the 29th ACM International Conference on Multimedia, 2021: 2167-2175. [8] JHA D, SMEDSRUD P H, RIEGLER M A, et al. ResUNet++: an advanced architecture for medical image segmentation[C]//Proceedings of 2019 IEEE International Symposium on Multimedia (ISM), 2019. [9] ZHANG Y, LIU H, HU Q. TransFuse: fusing transformers and CNNs for medical image segmentation[C]//Proceedings of 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), Strasbourg, France, September 27-October 1, 2021: 14-24. [10] DUC N T, OANH N T, THUY N T, et al. ColonFormer: an efficient transformer based method for colon polyp segmentation[J]. IEEE Access, 2022, 10: 80575-80586. [11] WU H, ZHAO Z, ZHONG J, et al. PolypSeg+: a lightweight context-aware network for real-time polyp segmentation[J]. IEEE Transactions on Cybernetics, 2022, 53(4): 2610-2621. [12] 周雪, 柏正尧, 陆倩杰, 等. 融合视觉Transformer和轴向注意的结直肠息肉分割[J]. 计算机工程与应用, 2023, 59(11): 222-230. ZHOU X, BAI Z Y, LU Q J, et al. Colorectal polyp segmentation combining pyramid vision Transformer and axial attention[J]. Computer Engineering and Applications, 2023, 59(11): 222-230. [13] SONG P, LI J, FAN H. Attention based multi-scale parallel network for polyp segmentation[J]. Computers in Biology and Medicine, 2022, 146: 105476. [14] TOMAR N K, JHA D, RIEGLER M A, et al. FANet: a feedback attention network for improved biomedical image segmentation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(11): 9375-9388. [15] SRIVASTAVA A, CHANDA S, JHA D, et al. GMSRF-Net: an improved generalizability with global multi-scale residual fusion network for polyp segmentation[C]//Proceedings of 2022 26th International Conference on Pattern Recognition (ICPR) , 2022: 4321-4327. [16] GALDRAN A, CARNEIRO G, BALLESTER M A G. Double encoder-decoder networks for gastrointestinal polyp segmentation[C]//Proceedings of International Conference on Pattern Recognition, January 10-15, 2021: 293-307. [17] TOMAR N K, JHA D, ALI S, et al. DDANet: dual decoder attention network for automatic polyp segmentation[C]//Proceedings of International Conference on Pattern Recognition, January 10-15, 2021: 307-314. [18] SWATI Z N K, ZHAO Q, KABIR M, et al. Brain tumor classification for MR images using transfer learning and fine-tuning[J]. Computerized Medical Imaging and Graphics, 2019, 75: 34-46. [19] ORBES-ARTEAGA M, VARSAVSKY T, SUDRE C H, et al. Multi-domain adaptation in brain MRI through paired consistency and adversarial learning[C]//Proceedings of First MICCAI Workshop on Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data, Shenzhen, China, October 13-17, 2019: 54-62. [20] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778. [21] CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 801-818. [22] LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2117-2125. [23] LIU S, QI L, QIN H, et al. Path aggregation network for instance segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 8759-8768. [24] GAO S H, CHENG M M, ZHAO K, et al. Res2net: a new multi-scale backbone architecture[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 43(2): 652-662. [25] WANG W, XIE E, LI X, et al. Pyramid vision transformer: a versatile backbone for dense prediction without convolutions[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 568-578. [26] REN S, ZHOU D, HE S, et al. Shunted self-attention via multi-scale token aggregation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 10853-10862. [27] JHA D, PIA H S, RIEGLER M, et al. Kvasir-SEG: a segmented polyp dataset[C]//International Conference on Multimedia Modeling, 2020: 451-462. [28] BERNAL J, SANCHEZ F J, FERNAND E G, et al. WM- DOVA maps for accurate polyp highlighting in colonoscopy: validation vs saliency maps from physicians[J]. Computerized Medical Imaging and Graphics, 2015, 43: 99-111. [29] TAJBAKHSH N, GURUDU S R, LIANG J. Automated polyp detection in colonoscopy videos using shape and context information[J]. IEEE Transactions on Medical Imaging, 2015, 35(2): 630-644. [30] VAZQUEZ D, BERNAL J, SANCHEZ F J, et al. A benchmark for endoluminal scene segmentation of colonoscopy images[J]. arXiv:1612.00799, 2016. [31] SILVA J, HISTACE A, ROMAIN O, et al. Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer[J]. International Journal of Computer Assisted Radiology and Surgery, 2014, 9(2): 283-293. [32] ZHOU Z, RAHMAN SIDDIQUEE M M, TAJBAKHSH N et al. Unet++: a nested U-Net architecture for medical image segmentation[C]//Proceedingds of 4th International Workshop on Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (DLMIA 2018) , Granada, Spain, September 20, 2018: 3-11. [33] HUANG C H, HUNG Y W, LIN Y. HarDNet-MSEG: a simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86?FPS[J]. arXiv:2101. 07172, 2021. [34] YIN Z, LIANG K, MA Z, GUO J. Duplex contextual relation network for polyp segmentation[C]//Proceedings of 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022: 1-5. [35] ZHANG R, LI G, LI Z, et al. Adaptive context selection for polyp segmentation[C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Oct 4-8 2020. Berlin, Heidelberg: Springer, 2020: 253-262. [36] WEI J, HU Y, ZHANG R, et al. Shallow attention network for polyp segmentation[C]//Proceedings of Intervention Conference on Medical Image Computing and Computer Assisted (MICCAI 2021), Strasbourg, France, September 27-October 1, 2021: 699-708. [37] PATEL K, BUR A M, WANG G. Enhanced U-Net: a feature enhancement network for polyp segmentation[C]//Proceedings of 2021 18th Conference on Robots and Vision (CRV), 2021: 181-188. |
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