activity
20162021
most cited3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

109 citations · 263 across the 12 of their papers we have counts for

collaborators

19 papers

cs.CV202142 cited

FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

Quande Liu, Cheng Chen, Jing Qin +2

Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trai…

cs.CV2021

Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment

Xueying Shi, Yueming Jin, Qi Dou +2

Automated surgical gesture recognition is of great importance in robot-assisted minimally invasive surgery. However, existing methods assume that training and testing data are from…

cs.CV20204 cited

Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation

Youyi Song, Lei Zhu, Baiying Lei +4

Segmenting overlapping cytoplasm of cells in cervical smear images is a clinically essential task, for quantitatively measuring cell-level features in order to diagnose cervical ca…

cs.CV2020

CNN in CT Image Segmentation: Beyound Loss Function for Expoliting Ground Truth Images

Youyi Song, Zhen Yu, Teng Zhou +4

Exploiting more information from ground truth (GT) images now is a new research direction for further improving CNN's performance in CT image segmentation. Previous methods focus o…

cs.CV20201 cited

Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion

Cheng Chen, Qi Dou, Yueming Jin +3

Accurate medical image segmentation commonly requires effective learning of the complementary information from multimodal data. However, in clinical practice, we often encounter th…

eess.IV202010 cited

Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation

Cheng Chen, Qi Dou, Hao Chen +2

Unsupervised domain adaptation has increasingly gained interest in medical image computing, aiming to tackle the performance degradation of deep neural networks when being deployed…