96 citations · 367 across the 7 of their papers we have counts for
7 papers
Visual Prompt Tuning for Test-time Domain Adaptation
Yunhe Gao, Xingjian Shi, Yi Zhu +5
Models should be able to adapt to unseen data during test-time to avoid performance drops caused by inevitable distribution shifts in real-world deployment scenarios. In this work,…
UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation
Yunhe Gao, Mu Zhou, Dimitris Metaxas
Transformer architecture has emerged to be successful in a number of natural language processing tasks. However, its applications to medical vision remain largely unexplored. In th…
FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images
Yunhe Gao, Rui Huang, Yiwei Yang +7
Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to avoid…
Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training
Yunhe Gao, Zhiqiang Tang, Mu Zhou +1
Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance. In medical…
CrossNorm and SelfNorm for Generalization under Distribution Shifts
Zhiqiang Tang, Yunhe Gao, Yi Zhu +3
Traditional normalization techniques (e.g., Batch Normalization and Instance Normalization) generally and simplistically assume that training and test data follow the same distribu…
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky +3
Data augmentation is one of the most important tools in training modern deep neural networks. Recently, great advances have been made in searching for optimal augmentation policies…