15 citations · 41 across the 8 of their papers we have counts for
13 papers · 1 filter
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,…
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…
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…