223 citations · 400 across the 21 of their papers we have counts for
21 papers
Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling
Fakai Wang, Kang Zheng, Le Lu +4
Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g. via Dual-…
Coherence Learning using Keypoint-based Pooling Network for Accurately Assessing Radiographic Knee Osteoarthritis
Kang Zheng, Yirui Wang, Chen-I Hsieh +4
Knee osteoarthritis (OA) is a common degenerate joint disorder that affects a large population of elderly people worldwide. Accurate radiographic assessment of knee OA severity pla…
Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph
Xiao-Yun Zhou, Bolin Lai, Weijian Li +12
Landmark localization plays an important role in medical image analysis. Learning based methods, including CNN and GCN, have demonstrated the state-of-the-art performance. However,…
Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray
Fakai Wang, Kang Zheng, Yirui Wang +6
Osteoporosis is a common chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, Dual-e…
Deep Implicit Statistical Shape Models for 3D Medical Image Delineation
Ashwin Raju, Shun Miao, Dakai Jin +3
3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and pro…
Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Images
Kang Zheng, Yirui Wang, Xiaoyun Zhou +8
Bone mineral density (BMD) is a clinically critical indicator of osteoporosis, usually measured by dual-energy X-ray absorptiometry (DEXA). Due to the limited accessibility of DEXA…
A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation
Xinyu Zhang, Yirui Wang, Chi-Tung Cheng +5
Object detection methods are widely adopted for computer-aided diagnosis using medical images. Anomalous findings are usually treated as objects that are described by bounding boxe…
Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization
Fakai Wang, Kang Zheng, Le Lu +3
Accurate vertebra localization and identification are required in many clinical applications of spine disorder diagnosis and surgery planning. However, significant challenges are p…
Contour Transformer Network for One-shot Segmentation of Anatomical Structures
Yuhang Lu, Kang Zheng, Weijian Li +8
Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gather…
Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rays
Yirui Wang, Kang Zheng, Chi-Tung Chang +7
Exploiting available medical records to train high performance computer-aided diagnosis (CAD) models via the semi-supervised learning (SSL) setting is emerging to tackle the prohib…
SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images
Ke Yan, Jinzheng Cai, Dakai Jin +7
Radiological images such as computed tomography (CT) and X-rays render anatomy with intrinsic structures. Being able to reliably locate the same anatomical structure across varying…
Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
Haomin Chen, Shun Miao, Daguang Xu +2
CXRs are a crucial and extraordinarily common diagnostic tool, leading to heavy research for CAD solutions. However, both high classification accuracy and meaningful model predicti…
Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images
Haomin Chen, Yirui Wang, Kang Zheng +8
Visual cues of enforcing bilaterally symmetric anatomies as normal findings are widely used in clinical practice to disambiguate subtle abnormalities from medical images. So far, i…
Learning to Segment Anatomical Structures Accurately from One Exemplar
Yuhang Lu, Weijian Li, Kang Zheng +8
Accurate segmentation of critical anatomical structures is at the core of medical image analysis. The main bottleneck lies in gathering the requisite expert-labeled image annotatio…
Structured Landmark Detection via Topology-Adapting Deep Graph Learning
Weijian Li, Yuhang Lu, Kang Zheng +8
Image landmark detection aims to automatically identify the locations of predefined fiducial points. Despite recent success in this field, higher-ordered structural modeling to cap…
Unsupervised Learning of Landmarks based on Inter-Intra Subject Consistencies
Weijian Li, Haofu Liao, Shun Miao +2
We present a novel unsupervised learning approach to image landmark discovery by incorporating the inter-subject landmark consistencies on facial images. This is achieved via an in…
Weakly Supervised Universal Fracture Detection in Pelvic X-rays
Yirui Wang, Le Lu, Chi-Tung Cheng +5
Hip and pelvic fractures are serious injuries with life-threatening complications. However, diagnostic errors of fractures in pelvic X-rays (PXRs) are very common, driving the dema…
Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation
Yue Zhang, Shun Miao, Tommaso Mansi +1
Automatic parsing of anatomical objects in X-ray images is critical to many clinical applications in particular towards image-guided invention and workflow automation. Existing dee…
Dilated FCN for Multi-Agent 2D/3D Medical Image Registration
Shun Miao, Sebastien Piat, Peter Fischer +4
2D/3D image registration to align a 3D volume and 2D X-ray images is a challenging problem due to its ill-posed nature and various artifacts presented in 2D X-ray images. In this p…
An Artificial Agent for Robust Image Registration
Rui Liao, Shun Miao, Pierre de Tournemire +4
3-D image registration, which involves aligning two or more images, is a critical step in a variety of medical applications from diagnosis to therapy. Image registration is commonl…
Real-time 2D/3D Registration via CNN Regression
Shun Miao, Z. Jane Wang, Rui Liao
In this paper, we present a Convolutional Neural Network (CNN) regression approach for real-time 2-D/3-D registration. Different from optimization-based methods, which iteratively…