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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…
Instance Shadow Detection
Tianyu Wang, Xiaowei Hu, Qiong Wang +2
Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named af…
Revisiting Shadow Detection: A New Benchmark Dataset for Complex World
Xiaowei Hu, Tianyu Wang, Chi-Wing Fu +3
Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world. Though recent shadow detectors have already achieved remarkable performance on…
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
José Ignacio Orlando, Huazhu Fu, João Barbossa Breda +28
Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modali…
FetusMap: Fetal Pose Estimation in 3D Ultrasound
Xin Yang, Wenlong Shi, Haoran Dou +6
The 3D ultrasound (US) entrance inspires a multitude of automated prenatal examinations. However, studies about the structuralized description of the whole fetus in 3D US are still…
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…
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis
Xueying Shi, Qi Dou, Cheng Xue +3
Automated skin lesion analysis is very crucial in clinical practice, as skin cancer is among the most common human malignancy. Existing approaches with deep learning have achieved…
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images
Yanning Zhou, Simon Graham, Navid Alemi Koohbanani +3
Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do this, it is important to consider the overall tis…
Joint Segmentation and Landmark Localization of Fetal Femur in Ultrasound Volumes
Xu Wang, Xin Yang, Haoran Dou +3
Volumetric ultrasound has great potentials in promoting prenatal examinations. Automated solutions are highly desired to efficiently and effectively analyze the massive volumes. Se…
IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation
Yanning Zhou, Hao Chen, Jiaqi Xu +2
Cell instance segmentation in Pap smear image remains challenging due to the wide existence of occlusion among translucent cytoplasm in cell clumps. Conventional methods heavily re…
Unifying Structure Analysis and Surrogate-driven Function Regression for Glaucoma OCT Image Screening
Xi Wang, Hao Chen, Luyang Luo +5
Optical Coherence Tomography (OCT) imaging plays an important role in glaucoma diagnosis in clinical practice. Early detection and timely treatment can prevent glaucoma patients fr…
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…
PU-GAN: a Point Cloud Upsampling Adversarial Network
Ruihui Li, Xianzhi Li, Chi-Wing Fu +2
Point clouds acquired from range scans are often sparse, noisy, and non-uniform. This paper presents a new point cloud upsampling network called PU-GAN, which is formulated based o…
Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video
Yueming Jin, Keyun Cheng, Qi Dou +1
Automatic instrument segmentation in video is an essentially fundamental yet challenging problem for robot-assisted minimally invasive surgery. In this paper, we propose a novel fr…
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…
Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis
Yueming Jin, Huaxia Li, Qi Dou +4
Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis and also very essential components in various a…
Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration
Lihao Liu, Xiaowei Hu, Lei Zhu +1
Brain image registration transforms a pair of images into one system with the matched imaging contents, which is of essential importance for brain image analysis. This paper presen…
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…
Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
Yi Wang, Haoran Dou, Xiaowei Hu +7
Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developi…
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…
Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis
Luyang Luo, Hao Chen, Xi Wang +5
Accurate and automatic analysis of breast MRI plays an important role in early diagnosis and successful treatment planning for breast cancer. Due to the heterogeneity nature, accur…
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…
PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis
Zixu Zhao, Huangjing Lin, Hao Chen +1
Automatic detection of cancer metastasis from whole slide images (WSIs) is a crucial step for following patient staging and prognosis. Recent convolutional neural network based app…
CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation
Yanning Zhou, Omer Fahri Onder, Qi Dou +3
Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatmen…