1.2k citations · 7.7k across the 159 of their papers we have counts for
25 papers · 1 filter
Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention
Mian Wu, Yinling Qian, Xiangyun Liao +2
Purpose: Segmentation of liver vessels from CT images is indispensable prior to surgical planning and aroused broad range of interests in the medical image analysis community. Due…
Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning
Danruo Deng, Guangyong Chen, Jianye Hao +2
The backpropagation networks are notably susceptible to catastrophic forgetting, where networks tend to forget previously learned skills upon learning new ones. To address such the…
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
Martin Wagner, Beat-Peter Müller-Stich, Anna Kisilenko +38
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the…
Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video
Jiacheng Wang, Yueming Jin, Liansheng Wang +3
Performing a real-time and accurate instrument segmentation from videos is of great significance for improving the performance of robotic-assisted surgery. We identify two importan…
Modelling Neighbor Relation in Joint Space-Time Graph for Video Correspondence Learning
Zixu Zhao, Yueming Jin, Pheng-Ann Heng
This paper presents a self-supervised method for learning reliable visual correspondence from unlabeled videos. We formulate the correspondence as finding paths in a joint space-ti…
Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling
Cheng Chen, Quande Liu, Yueming Jin +2
Domain adaptation typically requires to access source domain data to utilize their distribution information for domain alignment with the target data. However, in many real-world s…
SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning
Jiaqi Xu, Bin Li, Bo Lu +3
Autonomous surgical execution relieves tedious routines and surgeon's fatigue. Recent learning-based methods, especially reinforcement learning (RL) based methods, achieve promisin…
Accurate Grid Keypoint Learning for Efficient Video Prediction
Xiaojie Gao, Yueming Jin, Qi Dou +2
Video prediction methods generally consume substantial computing resources in training and deployment, among which keypoint-based approaches show promising improvement in efficienc…
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
Weiwen Liu, Feng Liu, Ruiming Tang +3
Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders. In Interactive Recommender Systems (IRS),…
Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
Quande Liu, Hongzheng Yang, Qi Dou +1
Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. However, despite existing FL algorithms o…
Point Cloud Upsampling via Disentangled Refinement
Ruihui Li, Xianzhi Li, Pheng-Ann Heng +1
Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent upsampling approaches aim to generate a dense point set, while achieving both distribution uni…
Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation
Cheng Xue, Qiao Deng, Xiaomeng Li +2
The superior performance of CNN on medical image analysis heavily depends on the annotation quality, such as the number of labeled image, the source of image, and the expert experi…
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images
Cheng Xue, Lei Zhu, Huazhu Fu +4
Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accu…
Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study
Luyang Luo, Hao Chen, Yongjie Xiao +10
Two DL models were developed using radiograph-level annotations (yes or no disease) and fine-grained lesion-level annotations (lesion bounding boxes), respectively named CheXNet an…
Deep Semi-supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection
Zhizhong Chai, Luyang Luo, Huangjing Lin +3
Deep learning has achieved unprecedented success in various object detection tasks with huge amounts of labeled data. However, obtaining large-scale annotations for medical images…
Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images
Yanwen Li, Luyang Luo, Huangjing Lin +2
The novel coronavirus disease 2019 (COVID-19) characterized by atypical pneumonia has caused millions of deaths worldwide. Automatically segmenting lesions from chest Computed Tomo…
OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays
Luyang Luo, Hao Chen, Yanning Zhou +2
Chest X-ray (CXR) is the most typical diagnostic X-ray examination for screening various thoracic diseases. Automatically localizing lesions from CXR is promising for alleviating r…
Temporal Memory Relation Network for Workflow Recognition from Surgical Video
Yueming Jin, Yonghao Long, Cheng Chen +3
Automatic surgical workflow recognition is a key component for developing context-aware computer-assisted systems in the operating theatre. Previous works either jointly modeled th…
One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video
Zixu Zhao, Yueming Jin, Bo Lu +4
Surgical instrument segmentation in robot-assisted surgery (RAS) - especially that using learning-based models - relies on the assumption that training and testing videos are sampl…
Future Frame Prediction for Robot-assisted Surgery
Xiaojie Gao, Yueming Jin, Zixu Zhao +2
Predicting future frames for robotic surgical video is an interesting, important yet extremely challenging problem, given that the operative tasks may have complex dynamics. Existi…
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…
Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer
Xiaojie Gao, Yueming Jin, Yonghao Long +2
Real-time surgical phase recognition is a fundamental task in modern operating rooms. Previous works tackle this task relying on architectures arranged in spatio-temporal order, ho…
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…
Deep Texture-Aware Features for Camouflaged Object Detection
Jingjing Ren, Xiaowei Hu, Lei Zhu +5
Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings. This paper presents to amplify the subtle texture diffe…