11 citations · 16 across the 3 of their papers we have counts for
3 papers
cs.CV2023★ 1 cited
SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation
Hyungseob Shin, Hyeongyu Kim, Sewon Kim +3
Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert a…
eess.IV2022★ 11 cited
COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training
Hyungseob Shin, Hyeongyu Kim, Sewon Kim +3
Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance when in fully supervised condition. However, acquiring pixel-level…
eess.IV2021★ 4 cited
Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation
Hyungseob Shin, Hyeongyu Kim, Sewon Kim +3
With the advances of deep learning, many medical image segmentation studies achieve human-level performance when in fully supervised condition. However, it is extremely expensive t…