309 citations · 409 across the 7 of their papers we have counts for
9 papers
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…
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…
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…
The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference
Fabian Balsiger, Alain Jungo, Naren Akash R J +12
The MICCAI conference has encountered tremendous growth over the last years in terms of the size of the community, as well as the number of contributions and their technical succes…
ICMSC: Intra- and Cross-modality Semantic Consistency for Unsupervised Domain Adaptation on Hip Joint Bone Segmentation
Guodong Zeng, Till D. Lerch, Florian Schmaranzer +6
Unsupervised domain adaptation (UDA) for cross-modality medical image segmentation has shown great progress by domain-invariant feature learning or image appearance translation. Ad…
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser +41
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are o…