Publications (4)
FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans
Fengze Liu, Yuyin Zhou, Elliot Fishman +1
Automatic abnormality detection in abdominal CT scans can help doctors improve the accuracy and efficiency in diagnosis. In this paper we aim at detecting pancreatic ductal adenoca…
Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation
Yuyin Zhou, Zhe Li, Song Bai +5
Accurate multi-organ abdominal CT segmentation is essential to many clinical applications such as computer-aided intervention. As data annotation requires massive human labor from…
Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation
Yuyin Zhou, Yingwei Li, Zhishuai Zhang +5
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phas…
Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation
Shuhao Fu, Yongyi Lu, Yan Wang +4
In this paper, we present a novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models t…