[1] M K BALWANT. A review on convolutional neural networks for brain tumor segmentation： methods， datasets， libraries， and future directions. IRBM, 43, 521-537(2022).

[2] D N LOUIS, A PERRY, P WESSELING et al. The 2021 WHO Classification of Tumors of the Central Nervous System： a summary. Neuro-oncology, 23, 1231-1251(2021).

[3] Y M A MOHAMMED, SEL GAROUANI, I JELLOULI. A survey of methods for brain tumor segmentation-based MRI images. Journal of Computational Design and Engineering, 10, 266-293(2023).

[4] Y GU, J Q CHI, J Q LIU et al. A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning. Computers in Biology and Medicine, 137, 104806(2021).

[5] Z H LIU, L TONG, L CHEN et al. Deep learning based brain tumor segmentation： a survey. Complex & Intelligent Systems, 9, 1001-1026(2023).

[6] [6] 谷宇. 基于深度卷积神经网络的CT影像肺结节检测技术研究［D］. 上海： 上海大学， 2019.GUY. Research on Lung Nodule Detection Based on Deep Convolutional Neural Network in Computed Tomography［D］.Shanghai： Shanghai University， 2019. （in Chinese）

[7] [7] 徐胜军， 张若暄， 孟月波， 等. 融合分形几何特征Resnet遥感图像建筑物分割［J］. 光学 精密工程， 2022， 30（16）： 2006-2020. doi: 10.37188/ope.20223016.2006XUS J， ZHANGR X， MENGY B， et al. Fusion of fractal geometric features Resnet remote sensing image building segmentation［J］. Opt. Precision Eng.， 2022， 30（16）： 2006-2020.（in Chinese）. doi: 10.37188/ope.20223016.2006

[8] O RONNEBERGER, P FISCHER, T BROX. U-Net： Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science, 234-241(2015).

[9] E SHELHAMER, J LONG, T DARRELL. Fully convolutional networks for semantic segmentation, 640-651(2017).

[10] Ö ÇIÇEK, A ABDULKADIR, S S LIENKAMP et al. 3D U-Net： Learning Dense Volumetric Segmentation from Sparse Annotation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, 424-432(2016).

[11] N IBTEHAZ, M S RAHMAN. MultiResUNet： Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 121, 74-87(2020).

[12] O OKTAY, J SCHLEMPER, L LE FOLGOC et al. Attention U-Net： Learning Where to Look for the Pancreas. arXiv, 1804-03999(2018). http：//arxiv.org/abs/1804.03999.pdf

[13] W WANG, C CHEN, M DING et al. Transbts： multimodal brain tumor segmentation using transformer, 109-119(2021).

[14] Y JIANG, Y ZHANG, X LIN et al. SwinBTS： a method for 3D multimodal brain tumor segmentation using swin transformer. Brain Sciences, 12, 797(2022).

[15] [15] 杨坚华， 张浩， 花海洋. 并行路径与强注意力机制遥感图像建筑物分割［J］. 光学 精密工程， 2023， 31（2）： 234-245. doi: 10.37188/ope.20233102.0234YANGJ H， ZHANGH， HUAH Y. Parallel path and strong attention mechanism for building segmentation in remote sensing images［J］. Opt. Precision Eng.， 2023， 31（2）： 234-245.（in Chinese）. doi: 10.37188/ope.20233102.0234

[16] [16] 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

[17] Z HUANG, Y W ZHAO, Y H LIU et al. GCAUNet： a group cross-channel attention residual UNet for slice based brain tumor segmentation. Biomedical Signal Processing and Control, 70, 102958(2021).

[18] N M ABOELENEIN, S H PIAO, A KOUBAA et al. HTTU-net： hybrid two track U-net for automatic brain tumor segmentation. IEEE Access, 8, 101406-101415(2020).

[19] X Q LU, Y GU, L D YANG et al. Multi-level 3D densenets for false-positive reduction in lung nodule detection based on chest computed tomography. Current Medical Imaging, 16, 1004-1021(2020).

[20] C CHEN, X P LIU, M DING et al. 3D Dilated Multi-Fiber Network for Real-Time Brain Tumor Segmentation in MRI. Lecture Notes in Computer Science, 184-192(2019).

[21] Z R LUO, Z D JIA, Z M YUAN et al. HDC-net： hierarchical decoupled convolution network for brain tumor segmentation. IEEE Journal of Biomedical and Health Informatics, 25, 737-745(2021).

[22] X Y ZHOU, X Y LI, K HU et al. ERV-Net： an efficient 3D residual neural network for brain tumor segmentation. Expert Systems with Applications, 170, 114566(2021).

[23] X T LI, A S YOU, Z ZHU et al. Semantic Flow for Fast and Accurate Scene Parsing. Computer Vision – ECCV 2020, 775-793(2020).

[24] X LI, Z S ZHONG, J L WU et al. Expectation-maximization attention networks for semantic segmentation, 9166-9175(2019).

[25] N NUECHTERLEIN, S MEHTA. 3D-ESPNet with pyramidal refinement for volumetric brain tumor image segmentation, 245-253(2019).

[26] J X ZHANG, Z K JIANG, J DONG et al. Attention gate ResU-net for automatic MRI brain tumor segmentation. IEEE Access, 8, 58533-58545(2020).

[27] N SHENG, D W LIU, J X ZHANG et al. Second-order ResU-Net for automatic MRI brain tumor segmentation. Mathematical Biosciences and Engineering： MBE, 18, 4943-4960(2021).

[28] A S AKBAR, C FATICHAH, N SUCIATI. Single level UNet3D with multipath residual attention block for brain tumor segmentation. Journal of King Saud University-Computer and Information Sciences, 34, 3247-3258(2022).

[29] H X LIU, G Q HUO, Q LI et al. Multiscale lightweight 3D segmentation algorithm with attention mechanism： brain tumor image segmentation. Expert Systems with Applications, 214, 119166(2023).

[30] Y K CHANG, Z Z ZHENG, Y W SUN et al. DPAFNet： a residual dual-path attention-fusion convolutional neural network for multimodal brain tumor segmentation. Biomedical Signal Processing and Control, 79, 104037(2023).