no code implementations • 6 Jan 2023 • Gangming Zhao, Kongming Liang, Chengwei Pan, Fandong Zhang, Xianpeng Wu, Xinyang Hu, Yizhou Yu
To tackle the challenges caused by the sparsity and anisotropy of vessels, a higher percentage of graph nodes are distributed in areas that potentially contain vessels while a higher percentage of edges follow the orientation of potential nearbyvessels.
no code implementations • 21 Apr 2022 • Churan Wang, Jing Li, Xinwei Sun, Fandong Zhang, Yizhou Yu, Yizhou Wang
To resolve this problem, we propose a novel framework, namely Domain Invariant Model with Graph Convolutional Network (DIM-GCN), which only exploits invariant disease-related features from multiple domains.
no code implementations • 8 Oct 2021 • Mingzhou Liu, Xinwei Sun, Fandong Zhang, Yizhou Yu, Yizhou Wang
Finally, to implement this contextual posterior, we introduce a Transformer that takes the object's information as a reference and locates correlated contextual factors.
no code implementations • 21 May 2021 • Yuhang Liu, Fandong Zhang, Chaoqi Chen, Siwen Wang, Yizhou Wang, Yizhou Yu
In this paper, we propose an Anatomy-aware Graph convolutional Network (AGN), which is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability.
no code implementations • 30 Sep 2020 • Chu-ran Wang, Jing Li, Fandong Zhang, Xinwei Sun, Hao Dong, Yizhou Yu, Yizhou Wang
Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations.
no code implementations • CVPR 2019 • Fandong Zhang, Ling Luo, Xinwei Sun, Zhen Zhou, Xiuli Li, Yizhou Yu, Yizhou Wang
Most of the previous mC detection methods belong to discriminative models, where classifiers are exploited to distinguish mCs from other backgrounds.
no code implementations • 21 Jul 2018 • Benyuan Sun, Zhen Zhou, Fandong Zhang, Xiuli Li, Yizhou Wang
Meanwhile, our sampling strategy halves the training time of the proposal network on LUNA16.