Edge-aware Hard Clustering Graph Pooling for Brain Imaging

1 code implementation • 23 Aug 2023 • Cheng Zhu, JiaYi Zhu, Xi Wu, Lijuan Zhang, Shuqi Yang, Ping Liang, Honghan Chen, Ying Tan

In this paper, we propose a novel edge-aware hard clustering graph pool (EHCPool), which is tailored to dominant edge features and redefines the clustering process.

Clustering Graph Clustering +1

Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge

1 code implementation • 10 Aug 2023 • Jun Ma, Yao Zhang, Song Gu, Cheng Ge, Shihao Ma, Adamo Young, Cheng Zhu, Kangkang Meng, Xin Yang, Ziyan Huang, Fan Zhang, Wentao Liu, YuanKe Pan, Shoujin Huang, Jiacheng Wang, Mingze Sun, Weixin Xu, Dengqiang Jia, Jae Won Choi, Natália Alves, Bram de Wilde, Gregor Koehler, Yajun Wu, Manuel Wiesenfarth, Qiongjie Zhu, Guoqiang Dong, Jian He, the FLARE Challenge Consortium, Bo wang

The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89. 5\%, 90. 9\%, and 88. 3\% on North American, European, and Asian cohorts, respectively.

Computed Tomography (CT)

Controllable Multi-Objective Re-ranking with Policy Hypernetworks

1 code implementation • 8 Jun 2023 • Sirui Chen, YuAn Wang, Zijing Wen, Zhiyu Li, Changshuo Zhang, Xiao Zhang, Quan Lin, Cheng Zhu, Jun Xu

In this paper, we propose a framework called controllable multi-objective re-ranking (CMR) which incorporates a hypernetwork to generate parameters for a re-ranking model according to different preference weights.

Recommendation Systems Re-Ranking

Temporal Dynamic Synchronous Functional Brain Network for Schizophrenia Diagnosis and Lateralization Analysis

1 code implementation • 31 Mar 2023 • Cheng Zhu, Ying Tan, Shuqi Yang, Jiaqing Miao, JiaYi Zhu, Huan Huang, Dezhong Yao, Cheng Luo

The available evidence suggests that dynamic functional connectivity (dFC) can capture time-varying abnormalities in brain activity in resting-state cerebral functional magnetic resonance imaging (rs-fMRI) data and has a natural advantage in uncovering mechanisms of abnormal brain activity in schizophrenia(SZ) patients.

AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?

1 code implementation • 28 Oct 2020 • Jun Ma, Yao Zhang, Song Gu, Cheng Zhu, Cheng Ge, Yichi Zhang, Xingle An, Congcong Wang, Qiyuan Wang, Xin Liu, Shucheng Cao, Qi Zhang, Shangqing Liu, Yunpeng Wang, Yuhui Li, Jian He, Xiaoping Yang

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets.

Continual Learning Organ Segmentation +2

Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography

no code implementations • 1 Mar 2020 • Li Xiao, Cheng Zhu, Junjun Liu, Chunlong Luo, Peifang Liu, Yi Zhao

It is worth mention that dense breast typically has a higher risk for developing breast cancers and also are harder for cancer detection in diagnosis, and our method outperforms a reported result from performance of radiologists.

Breast Cancer Detection object-detection +2

How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study

2 code implementations • MIDL 2019 • Jun Ma, Zhan Wei, Yiwen Zhang, Yixin Wang, Rongfei Lv, Cheng Zhu, Gaoxiang Chen, Jianan Liu, Chao Peng, Lei Wang, Yunpeng Wang, Jianan Chen

The \emph{second contribution} is that we systematically evaluated five benchmark methods on two representative public datasets.

Segmentation