activity
20202025
most citedOpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding

39 citations · 70 across the 9 of their papers we have counts for

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7 papers · 1 filter

eess.IV2024★ 4 cited

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…

eess.IV2023

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…

eess.IV2022★ 2 cited

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…

eess.IV2022

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…

eess.IV2022

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…

eess.IV2021★ 25 cited

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…