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25 papers · 1 filter
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
Pengfei Chen, Junjie Ye, Guangyong Chen +2
Supervised learning under label noise has seen numerous advances recently, while existing theoretical findings and empirical results broadly build up on the class-conditional noise…
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
Pengfei Chen, Junjie Ye, Guangyong Chen +2
For multi-class classification under class-conditional label noise, we prove that the accuracy metric itself can be robust. We concretize this finding's inspiration in two essentia…
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…
RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
Shujun Wang, Yaxi Zhu, Lequan Yu +5
The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis. However, due to the large scale of WSIs and various sizes of the abnormal area, how t…
DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets
Shujun Wang, Lequan Yu, Kang Li +3
Deep convolutional neural networks have significantly boosted the performance of fundus image segmentation when test datasets have the same distribution as the training datasets. H…
Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation
Kang Li, Lequan Yu, Shujun Wang +1
The success of deep convolutional neural networks is partially attributed to the massive amount of annotated training data. However, in practice, medical data annotations are usual…
Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation
Yanning Zhou, Hao Chen, Huangjing Lin +1
Deep learning methods show promising results for overlapping cervical cell instance segmentation. However, in order to train a model with good generalization ability, voluminous pi…
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization
Shujun Wang, Lequan Yu, Caizi Li +2
The generalization capability of neural networks across domains is crucial for real-world applications. We argue that a generalized object recognition system should well understand…
Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation
Kang Li, Shujun Wang, Lequan Yu +1
Medical image annotations are prohibitively time-consuming and expensive to obtain. To alleviate annotation scarcity, many approaches have been developed to efficiently utilize ext…
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…
Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains
Quande Liu, Qi Dou, Pheng-Ann Heng
Model generalization capacity at domain shift (e.g., various imaging protocols and scanners) is crucial for deep learning methods in real-world clinical deployment. This paper tack…
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.…
DNF-Net: a Deep Normal Filtering Network for Mesh Denoising
Xianzhi Li, Ruihui Li, Lei Zhu +2
This paper presents a deep normal filtering network, called DNF-Net, for mesh denoising. To better capture local geometry, our network processes the mesh in terms of local patches…
Deep Mining External Imperfect Data for Chest X-ray Disease Screening
Luyang Luo, Lequan Yu, Hao Chen +4
Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large dis…
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
Quande Liu, Lequan Yu, Luyang Luo +2
Training deep neural networks usually requires a large amount of labeled data to obtain good performance. However, in medical image analysis, obtaining high-quality labels for the…
Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes
Xin Yang, Xu Wang, Yi Wang +6
Background and Objective: Biometric measurements of fetal head are important indicators for maternal and fetal health monitoring during pregnancy. 3D ultrasound (US) has unique adv…
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…
PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
Ruihui Li, Xianzhi Li, Pheng-Ann Heng +1
We present PointAugment, a new auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification…
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…
A Rotation-Invariant Framework for Deep Point Cloud Analysis
Xianzhi Li, Ruihui Li, Guangyong Chen +3
Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to ar…
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…
Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search
Xiaojie Gao, Yueming Jin, Qi Dou +1
Automatic surgical gesture recognition is fundamental for improving intelligence in robot-assisted surgery, such as conducting complicated tasks of surgery surveillance and skill e…
MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data
Quande Liu, Qi Dou, Lequan Yu +1
Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress in this ta…
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…