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20162022
most cited3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

109 citations · 188 across the 20 of their papers we have counts for

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Showing 2020Show all

7 papers · 1 filter

cs.CV2020

Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery

Yonghao Long, Jie Ying Wu, Bo Lu +5

Automatic surgical gesture recognition is fundamentally important to enable intelligent cognitive assistance in robotic surgery. With recent advancement in robot-assisted minimally…

cs.CV20202 cited

Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video

Zixu Zhao, Yueming Jin, Xiaojie Gao +2

Performing low hertz labeling for surgical videos at intervals can greatly releases the burden of surgeons. In this paper, we study the semi-supervised instrument segmentation from…

cs.RO20201 cited

A Learning-Driven Framework with Spatial Optimization For Surgical Suture Thread Reconstruction and Autonomous Grasping Under Multiple Topologies and Environmental Noises

Bo Lu, Wei Chen, Yue-Ming Jin +5

Surgical knot tying is one of the most fundamental and important procedures in surgery, and a high-quality knot can significantly benefit the postoperative recovery of the patient.…

cs.LG20206 cited

LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow Recognition

Xueying Shi, Yueming Jin, Qi Dou +1

Automatic surgical workflow recognition in video is an essentially fundamental yet challenging problem for developing computer-assisted and robotic-assisted surgery. Existing appro…

cs.CV2020

Robust Medical Instrument Segmentation Challenge 2019

Tobias Ross, Annika Reinke, Peter M. Full +47

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tr…

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…