Optics and Precision Engineering, Volume. 31, Issue 18, 2700(2023)
Cross-scale and cross-dimensional adaptive transformer network for colorectal polyp segmentation
[1] LI W, ZHAO Y, LI F et al. MIA-Net: multi-information aggregation network combining transformers and convolutional feature learning for polyp segmentation[J]. Knowledge-Based Systems, 247, 108824(2022).
[2] [2] 徐昌佳, 易见兵, 曹锋, 等. 采用DoubleUNet网络的结直肠息肉分割算法[J]. 光学 精密工程, 2022, 30(8): 970-983. doi: 10.37188/ope.20223008.0970XUC J, YIJ B, CAOF, et al. Colorectal polyp segmentation algorithm using DoubleUNet network[J]. Opt. Precision Eng., 2022, 30(8): 970-983. (in Chinese). doi: 10.37188/ope.20223008.0970
[3] [3] 梁礼明, 周珑颂, 冯骏, 等. 基于高分辨率复合网络的皮肤病变分割[J]. 光学 精密工程, 2022, 30(16)2021-2038. doi: 10.37188/OPE.20223016.2021LIANGL M, ZHOUL S, FENGJ, et al. Skin lesion segmentation based on high-resolution composite network[J]. Opt. Precision Eng., 2022, 30(16)2021-2038(in Chinese). doi: 10.37188/OPE.20223016.2021
[4] LIANG H, CHENG Z, ZHONG H et al. A region-based convolutional network for nuclei detection and segmentation in microscopy images[J]. Biomedical Signal Processing and Control, 71, 103276(2022).
[5] SHAO D G, XU C R, XIANG Y et al. Ultrasound image segmentation with multilevel threshold based on differential search algorithm[J]. IET Image Processing, 13, 998-1005(2019).
[6] [6] 周明全, 杨稳, 林芃樾, 等. 基于最小二乘正则相关性分析的颅骨身份识别[J]. 光学 精密工程, 2021, 29(01):201-210. doi: 10.37188/OPE.20212901.0201ZHOUM Q, YANGW, LINP Y, et al. Skull identification based on least square canonical correlation analysis[J]. Opt. Precision Eng., 2021, 29(1): 201-210. (in Chinese). doi: 10.37188/OPE.20212901.0201
[7] [7] 梁礼明, 刘博文, 杨海龙, 等. 基于多特征融合的有监督视网膜血管提取[J]. 计算机学报, 2018, 41(11):2566-2580. doi: 10.11897/SP.J.1016.2018.02566LIANGL M, LIUB W, YANGH L, et al. Supervised blood vessel extraction in retinal images based on multiple feature fusion[J]. Chinese Journal of Computers, 2018, 41(11): 2566-2580.(in Chinese). doi: 10.11897/SP.J.1016.2018.02566
[8] RONNEBERGER O, FISCHER P, BROX T.
[9] SMEDSRUD P H, RIEGLER M A et al. ResUNet: an advanced architecture for medical image segmentation[C], 225-2255(9).
[10] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C], 7132-7141(18).
[12] LOU A G, GUAN S Y et al. CaraNet: context axial reverse attention network for segmentation of small medical objects[C], 12032, 81-92(2022).
[14] DAI Y, GAO Y F, LIU F Y. TransMed: transformers advance multi-modal medical image classification[J]. Diagnostics (Basel, Switzerland), 11, 1384(2021).
[16] WANG J F, HUANG Q M, TANG F L et al.
[17] WU C, LONG C, LI S et al. MSRAformer: Multiscale spatial reverse attention network for polyp segmentation[J]. Computers in Biology and Medicine, 151, 106274(2022).
[18] WANG W H, XIE E Z, LI X et al. PVT v2: improved baselines with pyramid vision transformer[J]. Computational Visual Media, 8, 415-424(2022).
[20] RUAN J C, XIANG S C, XIE M Y et al. MALUNet: a multi-attention and light-weight UNet for skin lesion segmentation[C], 1150-1156(6).
[22] [22] 刘媛媛, 周小康, 王跃勇, 等. 改进U-Net模型的保护性耕作田间秸秆覆盖检测[J]. 光学 精密工程, 2022, 30(9): 1101-1112. doi: 10.37188/OPE.20223009.1101LIUY Y, ZHOUX K, WANGY Y, et al. Straw coverage detection of conservation tillage farmland based on improved U-Net model[J]. Opt. Precision Eng., 2022, 30(9): 1101-1112. (in Chinese). doi: 10.37188/OPE.20223009.1101
[23] ZHANG W, FU C, ZHENG Y et al. HSNet: a hybrid semantic network for polyp segmentation[J]. Computers in Biology and Medicine, 150, 106173(2022).
[24] BERNAL J, SÁNCHEZ FJ, FERNÁNDEZ-ESPARRACH G et al. WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation
[25] SMEDSRUD P H, RIEGLER M A et al.
[26] TAJBAKHSH N, GURUDU S R, LIANG J M. Automated polyp detection in colonoscopy videos using shape and context information[J]. IEEE Transactions on Medical Imaging, 35, 630-644(2016).
[27] 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, 9, 283-293(2014).
[28] PATEL K, BUR A M, WANG G H. Enhanced U-Net: a feature enhancement network for polyp segmentation[C], 181-188(26).
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Liming LIANG, Anjun HE, Renjie LI, Jian WU. Cross-scale and cross-dimensional adaptive transformer network for colorectal polyp segmentation[J]. Optics and Precision Engineering, 2023, 31(18): 2700
Category: Information Sciences
Received: Mar. 15, 2023
Accepted: --
Published Online: Oct. 12, 2023
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