49 Result(s)
within
Conference Paper
Qi Dou
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Chapter and Conference Paper
Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks
Accurate acquisition of fetal ultrasound (US) standard planes is one of the most crucial steps in obstetric diagnosis. The conventional way of standard plane acquisition requires a thorough knowledge of fetal ...
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Chapter and Conference Paper
3D Fully Convolutional Networks for Intervertebral Disc Localization and Segmentation
Accurate localization and segmentation of intervertebral discs (IVDs) from volumetric data is a pre-requisite for clinical diagnosis and treatment planning. With the advance of deep learning, 2D fully convolut...
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Chapter and Conference Paper
Multi-scale and Modality Dropout Learning for Intervertebral Disc Localization and Segmentation
Automatic localization and segmentation of intervertebral discs (IVDs) from volumetric magnetic resonance (MR) images is important for spine disease diagnosis. It dramatically alleviates the workload of radiol...
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Chapter and Conference Paper
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper, we present a novel 3D deeply supervised ne...
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Chapter and Conference Paper
Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treatment of cardiovascular diseas...
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Chapter and Conference Paper
Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning
In this paper, we propose a novel framework with 3D convolutional networks (ConvNets) for automated detection of pulmonary nodules from low-dose CT scans, which is a challenging yet crucial task for lung cance...
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Chapter and Conference Paper
MTMR-Net: Multi-task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis
Lung cancer is the leading cause of cancer deaths worldwide. Early diagnosis of lung nodules is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accu...
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Chapter and Conference Paper
Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-Ray Segmentation
In spite of the compelling achievements that deep neural networks (DNNs) have made in medical image computing, these deep models often suffer from degraded performance when being applied to new test datasets w...
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Chapter and Conference Paper
Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis
Accurate and automatic analysis of breast MRI plays a vital role in early diagnosis and successful treatment planning for breast cancer. Due to the heterogeneity nature, precise diagnosis of tumors remains a c...
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Chapter and Conference Paper
IRNet: Instance Relation Network for Overlap** Cervical Cell Segmentation
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 rely on accurate nuclei detecti...
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Chapter and Conference Paper
A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network
Early diagnosis of prostate cancer is very crucial to reduce the mortality rate. Multi-parametric magnetic resonance imaging (MRI) can provide detailed visualization of prostate tissues and lesions. Their mali...
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Chapter and Conference Paper
Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video
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 framework to leverage instrumen...
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Chapter and Conference Paper
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis
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 remarkable performance on thi...
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Chapter and Conference Paper
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion
Accurate medical image segmentation commonly requires effective learning of the complementary information from multimodal data. However, in clinical practice, we often encounter the problem of missing imaging...
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Chapter and Conference Paper
Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels
Accurate, automated lesion detection in Computed Tomography (CT) is an important yet challenging task due to the large variation of lesion types, sizes, locations and appearances. Recent work on CT lesion dete...
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Chapter and Conference Paper
CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation
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 treatment. While it still remains cha...
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Chapter and Conference Paper
Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation
The superior performance of CNN on medical image analysis heavily depends on the annotation quality, such as the number of labeled images, the source of images, and the expert experience. The annotation requir...
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Chapter and Conference Paper
Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video
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 robotic surgical videos with...
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Chapter and Conference Paper
Shape Mask Generator: Learning to Refine Shape Priors for Segmenting Overlap** Cervical Cytoplasms
Segmenting overlap** cytoplasm of cervical cells plays a crucial role in cervical cancer screening. This task, however, is rather challenging, mainly because intensity (or color) information in the overlappi...
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Chapter and Conference Paper
Image-Level Harmonization of Multi-site Data Using Image-and-Spatial Transformer Networks
We investigate the use of image-and-spatial transformer networks (ISTNs) to tackle domain shift in multi-site medical imaging data. Commonly, domain adaptation (DA) is performed with little regard for explaina...
