Ziyuan Wang

发表

Near-Infrared Reflectance Imaging for Quantification of Atrophy associated with Age-related Macular Degeneration.

S. Sadda, Z. Hu, N. Abdelfattah, 2020, American journal of ophthalmology.

Automated segmentation and feature discovery of age-related macular degeneration and Stargardt disease via self-attended neural networks

Aaron Y. Lee, S. Sadda, Ziyuan Wang, 2022, Scientific Reports.

Delft University of Technology Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction

Marco Virgolin, Tanja Alderliesten, Ziyuan Wang, 2020 .

Automated segmentation of geographic atrophy using deep convolutional neural networks

Srinivas R. Sadda, Ziyuan Wang, Zhihong Hu, 2018, Medical Imaging.

Applications of graph convolutional networks in computer vision

Zeqi Zhu, Pingping Cao, Q. Niu, 2022, Neural Computing and Applications.

Visualizing and understanding inherent features in SD-OCT for the progression of age-related macular degeneration using deconvolutional neural networks

S. Sadda, Y. Kanagasingam, Z. Hu, 2020, Applied AI letters.

Deep learning for automated screening and semantic segmentation of age-related and juvenile atrophic macular degeneration

Srinivas R. Sadda, Ziyuan Wang, Zhihong Hu, 2019, Medical Imaging.

An End-to-End Network for Remote Sensing Imagery Semantic Segmentation via Joint Pixel- and Representation-Level Domain Adaptation

Zhenwei Shi, Ziyuan Wang, Bin Pan, 2021, IEEE Geoscience and Remote Sensing Letters.

Automatic generation of three-dimensional dose reconstruction data for two-dimensional radiotherapy plans for historically treated patients

Marco Virgolin, Tanja Alderliesten, Arjan Bel, 2020, Journal of medical imaging.

Automated Retinal Layer Segmentation Using Graph-based Algorithm Incorporating Deep-learning-derived Information

S. Sadda, Z. Hu, Ziyuan Wang, 2020, Scientific Reports.

Reverse engineering for reconstructing baseline features of dry age-related macular degeneration in optical coherence tomography

S. Sadda, M. Nittala, Ziyuan Wang, 2022, Scientific Reports.

Automatic Segmentation in Multiple OCT Layers For Stargardt Disease Characterization Via Deep Learning

Ziyuan Wang, Zhihong Hu, SriniVas R. Sadda, 2021, Translational vision science & technology.

Deep phenotyping of PROM1-associated retinal degeneration

M. Michaelides, R. Strauss, Navjit Singh, 2023, The British journal of ophthalmology.

Validation and Comparison of Radiograph-Based Organ Dose Reconstruction Approaches for Wilms Tumor Radiation Treatment Plans

P. Bosman, Choonsik Lee, C. Ronckers, 2022, Advances in radiation oncology.

Machine learning for automatic construction of pediatric abdominal phantoms for radiation dose reconstruction

Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman, 2020, Medical Imaging.

How do patient characteristics and anatomical features correlate to accuracy of organ dose reconstruction for Wilms’ tumor radiation treatment plans when using a surrogate patient’s CT scan?

Marco Virgolin, Tanja Alderliesten, Arjan Bel, 2019, Journal of radiological protection : official journal of the Society for Radiological Protection.

Automatic radiotherapy plan emulation for 3D dose reconstruction to enable big data analysis for historically treated patients

Peter A. N. Bosman, Marco Virgolin, Tanja Alderliesten, 2019, Medical Imaging.

Are age and gender suitable matching criteria in organ dose reconstruction using surrogate childhood cancer patients’ CT scans?

Tanja Alderliesten, Arjan Bel, Ziyuan Wang, 2018, Medical physics.

Artificial intelligence for assessment of Stargardt macular atrophy

Ziyuan Wang, Z. Hu, 2022, Neural Regeneration Research.

Using Ensemble OCT-Derived Features beyond Intensity Features for Enhanced Stargardt Atrophy Prediction with Deep Learning

S. Sadda, Ziyuan Wang, Z. Hu, 2023, Applied Sciences.

Automated choroidal segmentation in spectral optical coherence tomography images with geographic atrophy using multimodal complementary information

Srinivas R. Sadda, Ziyuan Wang, Zhihong Hu, 2019, Journal of Medical Imaging.

Locally-adaptive loosely-coupled level sets for retinal layer and fluid segmentation in subjects with central serous retinopathy

Jelena Novosel, Koen A. Vermeer, Lucas J. van Vliet, 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas

Jelena Novosel, Lucas J. van Vliet, Koenraad A. Vermeer, 2017, IEEE Transactions on Medical Imaging.

Automatic Data Augmentation Via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation

Yinghuan Shi, Dinggang Shen, Yang Gao, 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms pdf

Peter A. N. Bosman, Marco Virgolin, Tanja Alderliesten, 2019, ArXiv.

Automated Retinal Layer Segmentation Using Graph-based Algorithm Incorporating Deep-learning-derived Information

Zubin Mishra, Anushika Ganegoda, Jane Selicha, 2020, Scientific Reports.

An Image Denoising Method Based on Deep Residual GAN

Lidan Wang, Shukai Duan, Yunfei Li, 2020, Journal of Physics: Conference Series.