Optics and Precision Engineering, Volume. 30, Issue 16, 2021(2022)
Skin lesion segmentation based on high-resolution composite network
[1] Z H WEI, F SHI, H SONG et al. Attentive boundary aware network for multi-scale skin lesion segmentation with adversarial training. Multimedia Tools and Applications, 79, 27115-27136(2020).
[2] [2] 2任凤雷, 何昕, 魏仲慧, 等. 基于DeepLabV3+与超像素优化的语义分割[J]. 光学 精密工程, 2019, 27(12): 2722-2729. doi: 10.3788/ope.20192712.2722RENF L, HEX, WEIZ H, et al. Semantic segmentation based on DeepLabV3+ and superpixel optimization[J]. Opt. Precision Eng., 2019, 27(12): 2722-2729.(in Chinese). doi: 10.3788/ope.20192712.2722
[3] [3] 3秦传波, 宋子玉, 曾军英, 等. 联合多尺度和注意力-残差的深度监督乳腺癌分割[J]. 光学 精密工程, 2021, 29(4): 877-895. doi: 10.37188/OPE.20212904.0877QINC B, SONGZ Y, ZENGJ Y, et al. Deeply supervised breast cancer segmentation combined with multi-scale and attention-residuals[J]. Opt. Precision Eng., 2021, 29(4): 877-895.(in Chinese). doi: 10.37188/OPE.20212904.0877
[4] C ZHAO, R J SHUAI, L MA et al. Segmentation of skin lesions image based on U-Net + +. Multimedia Tools and Applications, 81, 8691-8717(2022).
[5] S GARG, B JINDAL. Skin lesion segmentation using k-mean and optimized fire fly algorithm. Multimedia Tools and Applications, 80, 7397-7410(2021).
[6] [6] 6邹永宁, 张智斌, 李琦, 等. 基于Hessian矩阵和支持向量机的CT图像裂纹分割[J]. 光学 精密工程, 2021, 29(10): 2517-2527. doi: 10.37188/OPE.2021.0349ZOUY N, ZHANGZ B, LIQ, et al. Crack detection and segmentation in CT images using Hessian matrix and support vector machine[J]. Opt. Precision Eng., 2021, 29(10): 2517-2527.(in Chinese). doi: 10.37188/OPE.2021.0349
[7] O RONNEBERGER, P FISCHER, T BROX. U-net: convolutional networks for biomedical image segmentation, 234-241(2015).
[9] A RADMAN, A SALLAM, S A SUANDI. Deep residual network for face sketch synthesis. Expert Systems With Applications, 190, 115980(2022).
[10] S BAGHERSALIMI, B BOZORGTABAR, P SCHMID-SAUGEON et al. DermoNet: densely linked convolutional neural network for efficient skin lesion segmentation. EURASIP Journal on Image and Video Processing, 1-10(2019).
[11] Z H WEI, H SONG, L CHEN et al. Attention-based DenseUnet network with adversarial training for skin lesion segmentation. IEEE Access, 7, 136616-136629(2019).
[12] M M K SARKER, H A RASHWAN, F AKRAM et al. SLSDeep: skin lesion segmentation based on dilated residual and pyramid pooling networks, 21-29(2018).
[13] [13] 13孟颖, 田启川, 吴施瑶. 基于U型网络复合特征的视网膜血管分割方法[J]. 计算机应用与软件, 2021, 38(8): 227-232, 267. doi: 10.3969/j.issn.1000-386x.2021.08.035MENGY, TIANQ C, WUS Y. Retinal vessel segmentation method of composite feature based on u-net[J]. Computer Applications and Software, 2021, 38(8): 227-232, 267.(in Chinese). doi: 10.3969/j.issn.1000-386x.2021.08.035
[15] S SINGH, S KRISHNAN. Filter response normalization layer: eliminating batch dependence in the training of deep neural networks, 11234-11243(2020).
[16] G HUANG, Z LIU, L VAN DER MAATEN et al. Densely connected convolutional networks, 4700-4708(2017).
[17] Q B HOU, L ZHANG, M M CHENG et al. Strip pooling: rethinking spatial pooling for scene parsing, 4002-4011(2020).
[18] H S ZHAO, J P SHI, X J QI et al. Pyramid scene parsing network, 6230-6239(2017).
[19] [19] 19汪水源, 侯志强, 王囡, 等. 基于自适应模板更新与多特征融合的视频目标分割算法[J]. 光电工程, 2021, 48(10): 210193.WANGS Y, HOUZ Q, WANGN, et al. Video object segmentation algorithm based on adaptive template updating and multi-feature fusion[J]. Opto-Electronic Engineering, 2021, 48(10): 210193.(in Chinese)
[20] S S M SALEHI, D ERDOGMUS, A GHOLIPOUR. Tversky loss function for image segmentation using 3D fully convolutional deep networks, 379-387(2017).
[21] D GUTMAN, N C F CODELLA, E CELEBI et al. Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXive:1605, 01397(2016).
[22] N C F CODELLA, D GUTMAN, E CELEBI 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), 168-172(2018).
[23] P TSCHANDL, C ROSENDAHL, H KITTLER. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data, 5, 180161(2018).
[24] T MENDONÇA, P M FERREIRA, J S MARQUES et al. PH2 - A dermoscopic image database for research and benchmarking, 5437-5440(2013).
[25] S QAMAR, P AHMAD, L L SHEN. Dense encoder-decoder-based architecture for skin lesion segmentation. Cognitive Computation, 13, 583-594(2021).
[26] R GU, G T WANG, T SONG et al. CA-net: comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Transactions on Medical Imaging, 40, 699-711(2021).
[27] [27] 27杨国亮, 洪志阳, 王志元, 等. 基于改进全卷积网络的皮肤病变图像分割[J]. 计算机工程与设计, 2018, 39(11): 3500-3505.YANGG L, HONGZ Y, WANGZ Y, et al. Image segmentation of skin lesions based on improved fully convolution network[J]. Computer Engineering and Design, 2018, 39(11): 3500-3505.(in Chinese)
[28] Y Y DONG, L J WANG, S L CHENG et al. FAC-net: feedback attention network based on context encoder network for skin lesion segmentation. Sensors (Basel, Switzerland), 21, 5172(2021).
[29] Z MIRIKHARAJI, S IZADI, J KAWAHARA et al. Deep auto-context fully convolutional neural network for skin lesion segmentation, 877-880(2018).
[30] X Z TONG, J Y WEI, B SUN et al. ASCU-net: attention gate, spatial and channel attention U-net for skin lesion segmentation. Diagnostics (Basel, Switzerland), 11, 501(2021).
[31] E NASR-ESFAHANI, S RAFIEI, M H JAFARI et al. Dense pooling layers in fully convolutional network for skin lesion segmentation. Computerized Medical Imaging and Graphics, 78, 101658(2019).
[32] F Y XIE, J W YANG, J LIU et al. Skin lesion segmentation using high-resolution convolutional neural network. Computer Methods and Programs in Biomedicine, 186, 105241(2020).
[33] Y J TANG, F YANG, S F YUAN et al. A multi-stage framework with context information fusion structure for skin lesion segmentation, 1407-1410(2019).
[34] S VESAL, N RAVIKUMAR, A MAIER. SkinNet: a deep learning framework for skin lesion segmentation, 1-3(2018).
[35] H M ÜNVER, E AYAN. Skin lesion segmentation in dermoscopic images with combination of YOLO and GrabCut algorithm. Diagnostics (Basel, Switzerland), 9, 72(2019).
[36] B Y LEI, Z M XIA, F JIANG et al. Skin lesion segmentation via generative adversarial networks with dual discriminators. Medical Image Analysis, 64, 101716(2020).
[37] Q G JIN, H CUI, C M SUN et al. Cascade knowledge diffusion network for skin lesion diagnosis and segmentation. Applied Soft Computing, 99, 106881(2021).
[38] R RAMADAN. CU-net: a new improved multi-input color U-net model for skin lesion semantic segmentation. IEEE Access, 10, 15539-15564(2022).
[39] H S WU, J Q PAN, Z Y LI et al. Automated skin lesion segmentation via an adaptive dual attention module. IEEE Transactions on Medical Imaging, 40, 357-370(2021).
[40] K HU, J LU, D J LEE et al. AS-net: attention synergy network for skin lesion segmentation. Expert Systems With Applications, 201, 117112(2022).
Get Citation
Copy Citation Text
Liming LIANG, Longsong ZHOU, Jun FENG, Xiaoqi SHENG, Jian WU. Skin lesion segmentation based on high-resolution composite network[J]. Optics and Precision Engineering, 2022, 30(16): 2021
Category: Information Sciences
Received: Mar. 13, 2022
Accepted: --
Published Online: Sep. 22, 2022
The Author Email: WU Jian (wujian@jxust.edu.cn)