科学研究
人工智能与医学图像处理：
图像分割、领域自适应、联邦学习
-
[1] Z. Yan, X. Yang, and K. -T. Cheng. “A Deep Model with Shape-Preserving Loss for Gland Instance Segmentation.” International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 138-146, 2018..
-
[2] Z. Yan, X. Yang, and K. -T. Cheng. “A Skeletal Similarity Metric for Quality Evaluation of Vessel Segmentation.” IEEE Transactions on Medical Imaging, 37 (4), 1045-1057, 2018..
-
[3] Z. Yan, X. Yang, and K. -T. Cheng. “Joint Segment-Level and Pixel-Wise Losses for Deep Learning based Retinal Vessel Segmentation.” IEEE Transactions on Biomedical Engineering, 65 (9), 1912-1923, 2018. (ESI Highly Cited Paper).
-
[4] Z. Yan, X. Yang, and K. -T. Cheng. “A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation.” IEEE Journal of Biomedical and Health Informatics, 23 (4), 1427-1436, 2019. (ESI Highly Cited Paper).
-
[5] Z. Yan, X. Yang, and K. -T. Cheng. “Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework.” IEEE Transactions on Medical Imaging, 39 (6), 2176-2189, 2020..