316 citations · 2.9k across the 50 of their papers we have counts for
9 papers · 1 filter
CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading
Xiaomeng Li, Xiaowei Hu, Lequan Yu +3
Diabetic retinopathy (DR) and diabetic macular edema (DME) are the leading causes of permanent blindness in the working-age population. Automatic grading of DR and DME helps ophtha…
Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound
Haoran Dou, Xin Yang, Jikuan Qian +8
Standard plane localization is crucial for ultrasound (US) diagnosis. In prenatal US, dozens of standard planes are manually acquired with a 2D probe. It is time-consuming and oper…
Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation
Shujun Wang, Lequan Yu, Kang Li +3
Accurate segmentation of the optic disc (OD) and cup (OC)in fundus images from different datasets is critical for glaucoma disease screening. The cross-domain discrepancy (domain s…
Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
Lequan Yu, Shujun Wang, Xiaomeng Li +2
Training deep convolutional neural networks usually requires a large amount of labeled data. However, it is expensive and time-consuming to annotate data for medical image segmenta…
Revisiting Metric Learning for Few-Shot Image Classification
Xiaomeng Li, Lequan Yu, Chi-Wing Fu +2
The goal of few-shot learning is to recognize new visual concepts with just a few amount of labeled samples in each class. Recent effective metric-based few-shot approaches employ…
Difficulty-aware Meta-learning for Rare Disease Diagnosis
Xiaomeng Li, Lequan Yu, Yueming Jin +3
Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data. Hence, to train a neural network to classify rare diseases with a…
Unsupervised Detection of Distinctive Regions on 3D Shapes
Xianzhi Li, Lequan Yu, Chi-Wing Fu +2
This paper presents a novel approach to learn and detect distinctive regions on 3D shapes. Unlike previous works, which require labeled data, our method is unsupervised. We conduct…
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation
Xiaomeng Li, Lequan Yu, Hao Chen +3
Deep convolutional neural networks have achieved remarkable progress on a variety of medical image computing tasks. A common problem when applying supervised deep learning methods…
Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
Shujun Wang, Lequan Yu, Xin Yang +2
Glaucoma is a leading cause of irreversible blindness. Accurate segmentation of the optic disc (OD) and cup (OC) from fundus images is beneficial to glaucoma screening and diagnosi…