1.医学影像分析，主要应用场景包括基于多模态影像的乳腺癌辅助诊断算法和智能牙科分析算法研究

2.基于深度学习的图像和视频处理算法研究及应用，包括压缩、插帧、超分、生成等算法的研究集及应用

3.多模态大模型相关，包括大模型训练、大模型轻量化以及在医学影像分析领域的应用

2020.6-至今 上海理工大学健康科学与工程学院，医学信息工程研究所，副教授

2014.12-2020.6 上海理工大学医疗器械与食品学院，医学信息工程研究所，讲师

2020.3-2021.12 上海科技大学生物医学工程学院沈定刚教授IDEA LAB，访问学者

2008.9-2014.11 上海交通大学电子信息与电气工程学院，硕士/博士，导师杨杰教授，关新平教授

2004.9-2008.6 武汉理工大学，本科

1.医学影像分析，主要应用场景包括基于多模态影像的乳腺癌辅助诊断算法和智能牙科分析算法研究

2.基于深度学习的图像和视频处理算法研究及应用，包括压缩、插帧、超分、生成等算法的研究集及应用

3.多模态大模型相关，包括大模型训练、大模型轻量化以及在医学影像分析领域的应用

1、国家自然科学基金，61906121，基于深度无监督分簇的混合监督图像语义分割方法研究

2、国家自然科学基金，82300122，基于影像组学及图像分割技术的胸腺病理一体化诊断的研究

3、广东省“新一代人工智能重大专项”，2021B0101420006，“基于深度学习多组学的乳腺癌辅助诊疗与预后预测系统”

1.Zhou, L *(第一作者/通讯作者), Cai C, Gao Y, et al. Variational Autoencoder for Low Bit-rate Image Compression[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR). 2018: 2617-2620 

2.Zhou, L *(第一作者/通讯作者), Sun, Z., Wu, XJ. and Wu, J., End-to-end Optimized Image Compression with Attention Mechanism, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019 (CVPR)

3.Wu, X., Zhang, Z., Feng, J., Zhou, L. *(通讯作者), & Wu, J. (2020). End-to-end Optimized Video Compression with MV-Residual Prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPR) (pp. 156-157).

4.Zhou, L. *(第一作者/通讯作者), Fu, K., Liu, Z., Zhang, F., Yin, Z., & Zheng, J. (2019). Superpixel based continuous conditional random field neural network for semantic segmentation. Neurocomputing, 340, 196-210.

5.Zhou, L. *(第一作者/通讯作者), Kong, X., Gong, C., Zhang, F., & Zhang, X. (2020). FC-RCCN: Fully convolutional residual continuous CRF network for semantic segmentation.Pattern Recognition Letters,130,54-63.

6.Zhou, L. *(第一作者/通讯作者), & Wei, W. (2020). DIC: deep image clustering for unsupervised image segmentation.IEEE Access, 8, 34481-34491.

7.Zhou, L. *(第一作者/通讯作者), Gong, C., Liu, Z., & Fu, K. (2020). SAL: Selection and attention losses for weakly supervised semantic segmentation. IEEE Transactions on Multimedia, 23, 1035-1048.

8.Zhou, L. *(第一作者/通讯作者), Chen, H., Wei, Y., & Li, X. (2022). Mining Confident Supervision by Prototypes Discovering and Annotation Selection for Weakly Supervised Semantic Segmentation. Neurocomputing. 2022,501(501):420-435

9.Zhou, L. *(第一作者/通讯作者), Wang, S., Sun, K., Zhou, T., Yan, F., & Shen, D. (2022). Three-dimensional affinity learning based multi-branch ensemble network for breast tumor segmentation in MRI. Pattern Recognition, 129,108723.

10.陈骅桂, 周雷（通讯作者), 丛志洋, 赵廉(2022), 基于多维信息融合的无监督快速肺部血管分割算法,《生物医学工程学报》，24(03), 289-300

11.周威；张昱中；罗晶；周雷（通讯作者)(2023)，“面向胸部骨分割的混合双编码器模型及应用”，《小型微型计算机系统》

12.Liu, W., Zhou, L., & Yang, X. (2023). High-order features of a single linear corneal laceration image are valuable biomarkers in an intelligent multimodal analytic strategy for corneal laceration reconstruction. Displays, 79, 102507.

13.Zhang, J., Cui, Z., Shi, Z., Jiang, Y., Zhang, Z., Dai, X., Zhou, L.. & Shen, D.(2023). A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework. Cell Patterns, 4(9).

14.Chen, Q., Zhang, J., Meng, R., Zhou, L., Li, Z., Feng, Q., & Shen, D. (2024). Modality-Specific Information Disentanglement from Multi-parametric MRI for Breast Tumor Segmentation and Computer-aided Diagnosis.IEEE Transactions on Medical Imaging, DOI:10.1109/TMI.2024.3352648

15.Zhou, L*(第一作者/通讯作者), Zhang, Y., Zhang, J., ... & Shen, D. (2024). Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI.IEEE Transactions on Medical Imaging. DOI:10.1109/TMI.2024.3435450

16. Yang, M., Zhang, T., Song, X., Zhang, Y., & Zhou, L. (2025). Semi-supervised pre-training based multi-task network for thyroid-associated ophthalmopathy classification. Displays, 87, 102974.

17.Zheng, L., Zhang, Y., Qu, T., Wang, X., & Zhou, L. (2025). ECLNet: Efficient convolution with lite transformer for thymoma segmentation. Displays, 103091.

18. Liu, Q., Qu, T., Xu, X., Li, X., Lin, D., Liu, H., ... & Wang, X. (2025). Automated segmentation by SCA-UNet can be directly used for radiomics diagnosis of thymic epithelial tumors. European Journal of Radiology, 185, 112004.

19.Zhou, L., Wang, J., Luo, J., Guo, Y., & Li, X. (2025). Optimizing multi-task network with learned prototypes for weakly supervised semantic segmentation. Signal Processing: Image Communication, 134, 117272.