8 citations · 23 across the 7 of their papers we have counts for
7 papers
MMViT: Multiscale Multiview Vision Transformers
Yuchen Liu, Natasha Ong, Kaiyan Peng +8
We present Multiscale Multiview Vision Transformers (MMViT), which introduces multiscale feature maps and multiview encodings to transformer models. Our model encodes different vie…
SVT: Supertoken Video Transformer for Efficient Video Understanding
Chenbin Pan, Rui Hou, Hanchao Yu +3
Whether by processing videos with fixed resolution from start to end or incorporating pooling and down-scaling strategies, existing video transformers process the whole video conte…
Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels
Yuqian Zhou, Hanchao Yu, Humphrey Shi
Retinal vessel segmentation from retinal images is an essential task for developing the computer-aided diagnosis system for retinal diseases. Efforts have been made on high-perform…
Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks
Qiaoying Huang, Eric Z. Chen, Hanchao Yu +4
Accurate estimation of shape thickness from medical images is crucial in clinical applications. For example, the thickness of myocardium is one of the key to cardiac disease diagno…
Anatomy-Aware Cardiac Motion Estimation
Pingjun Chen, Xiao Chen, Eric Z. Chen +3
Cardiac motion estimation is critical to the assessment of cardiac function. Myocardium feature tracking (FT) can directly estimate cardiac motion from cine MRI, which requires no…
Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation
Hanchao Yu, Xiao Chen, Humphrey Shi +3
Cardiac motion estimation plays a key role in MRI cardiac feature tracking and function assessment such as myocardium strain. In this paper, we propose Motion Pyramid Networks, a n…
FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation
Hanchao Yu, Shanhui Sun, Haichao Yu +4
Motion estimation of cardiac MRI videos is crucial for the evaluation of human heart anatomy and function. Recent researches show promising results with deep learning-based methods…