39 citations · 88 across the 11 of their papers we have counts for
11 papers
Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functions
Kangxian Xie, Yufei Zhu, Kaiming Kuang +4
High-quality 3D reconstruction of pulmonary segments plays a crucial role in segmentectomy and surgical planning for the treatment of lung cancer. Due to the resolution requirement…
CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation
Jun Wang, Yuzhe Qin, Kaiming Kuang +4
We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks. By incorporating extensive data augmentat…
Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge
Jiancheng Yang, Rui Shi, Liang Jin +22
Rib fractures are a common and potentially severe injury that can be challenging and labor-intensive to detect in CT scans. While there have been efforts to address this field, the…
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
Minghua Liu, Ruoxi Shi, Kaiming Kuang +6
We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framew…
SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention
Rui Xu, Zhi Liu, Yong Luo +5
Lung cancer is the leading cause of cancer death worldwide. The best solution for lung cancer is to diagnose the pulmonary nodules in the early stage, which is usually accomplished…
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
Liang Jin, Shixuan Gu, Donglai Wei +7
Automatic rib labeling and anatomical centerline extraction are common prerequisites for various clinical applications. Prior studies either use in-house datasets that are inaccess…
LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection
Rui Xu, Yong Luo, Bo Du +2
Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection. However, existing CNN based pulmonary nodule detectio…
What Makes for Automatic Reconstruction of Pulmonary Segments
Kaiming Kuang, Li Zhang, Jingyu Li +4
3D reconstruction of pulmonary segments plays an important role in surgical treatment planning of lung cancer, which facilitates preservation of pulmonary function and helps ensure…
Asymmetric 3D Context Fusion for Universal Lesion Detection
Jiancheng Yang, Yi He, Kaiming Kuang +3
Modeling 3D context is essential for high-performance 3D medical image analysis. Although 2D networks benefit from large-scale 2D supervised pretraining, it is weak in capturing 3D…
MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response
Jiancheng Yang, Jiajun Chen, Kaiming Kuang +3
Predicting clinical outcome is remarkably important but challenging. Research efforts have been paid on seeking significant biomarkers associated with the therapy response or/and p…
Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study
Jiancheng Yang, Mingze Gao, Kaiming Kuang +4
Diagnosis of pulmonary lesions from computed tomography (CT) is important but challenging for clinical decision making in lung cancer related diseases. Deep learning has achieved g…