316 citations · 2.9k across the 50 of their papers we have counts for
12 papers · 1 filter
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 Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images
Lequan Yu, Zhicheng Zhang, Xiaomeng Li +1
Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence…
Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis
Xiaomeng Li, Mengyu Jia, Md Tauhidul Islam +2
The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making. However, developing such automatic solutions is challenging…
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…
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Yue Sun, Kun Gao, Zhengwang Wu +30
To better understand early brain growth patterns in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gr…
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Yingda Xia, Dong Yang, Zhiding Yu +7
Although having achieved great success in medical image segmentation, deep learning-based approaches usually require large amounts of well-annotated data, which can be extremely ex…
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…
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…