B
Biting Yu
Researcher at University of Wollongong
Publications - 14
Citations - 797
Biting Yu is an academic researcher from University of Wollongong. The author has contributed to research in topics: Segmentation & Modality (human–computer interaction). The author has an hindex of 7, co-authored 13 publications receiving 420 citations. Previous affiliations of Biting Yu include Information Technology University.
Papers
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Journal ArticleDOI
3D conditional generative adversarial networks for high-quality PET image estimation at low dose
Yan Wang,Biting Yu,Lei Wang,Chen Zu,David S. Lalush,Weili Lin,Xi Wu,Jiliu Zhou,Dinggang Shen,Dinggang Shen,Luping Zhou,Luping Zhou +11 more
TL;DR: Experimental results show that the proposed 3D c‐GANs method outperforms the benchmark methods and achieves much better performance than the state‐of‐the‐art methods in both qualitative and quantitative measures.
Journal ArticleDOI
Ea-GANs: Edge-Aware Generative Adversarial Networks for Cross-Modality MR Image Synthesis
TL;DR: The experimental results demonstrate that the proposed edge-aware generative adversarial networks (Ea-GANs) outperform multiple state-of-the-art methods for cross-modality MR image synthesis in both qualitative and quantitative measures.
Journal ArticleDOI
3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis
Yan Wang,Luping Zhou,Biting Yu,Lei Wang,Chen Zu,David S. Lalush,Weili Lin,Xi Wu,Jiliu Zhou,Dinggang Shen +9 more
TL;DR: Experimental results show that the proposed 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) outperforms the traditional multi- modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.
Proceedings ArticleDOI
3D cGAN based cross-modality MR image synthesis for brain tumor segmentation
TL;DR: This paper investigates whether synthesizing FLAIR images from T1 could help improve brain tumor segmentation from the single modality of T1 by designing a 3D conditional Generative Adversarial Network (cGAN) for FLAIR image synthesis and a local adaptive fusion method to better depict the details of the synthesized FL AIR images.
Book ChapterDOI
Medical Image Synthesis via Deep Learning.
TL;DR: This chapter will focus on introducing typical CNNs and GANs models for medical image synthesis, and elaborate the recent work about low-dose to high-dose PET image synthesisation, and cross-modality MR images synthesis, using these models.