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20172022
most citedGT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation

57 citations · 90 across the 7 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

cs.CV2021★ 2 cited

Dispensed Transformer Network for Unsupervised Domain Adaptation

Yunxiang Li, Jingxiong Li, Ruilong Dan +10

Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. Howeve…

cs.CV2021★ 57 cited

GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation

Yunxiang Li, Shuai Wang, Jun Wang +5

To achieve an accurate assessment of root canal therapy, a fundamental step is to perform tooth root segmentation on oral X-ray images, in that the position of tooth root boundary…

cs.CV2021★ 7 cited

AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy

Yunxiang Li, Guodong Zeng, Yifan Zhang +10

Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results wi…

cs.CV2021

High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection

Yunxiang Li, Yifan Zhang, Yaqi Wang +7

As the most economical and routine auxiliary examination in the diagnosis of root canal treatment, oral X-ray has been widely used by stomatologists. It is still challenging to seg…

eess.IV2021★ 2 cited

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images

Jingxiong Li, Yaqi Wang, Shuai Wang +4

Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification…