activity
20172021
most citedCE-Net: Context Encoder Network for 2D Medical Image Segmentation

2.3k citations · 3.9k across the 30 of their papers we have counts for

collaborators

49 papers

eess.IV20213 cited

Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images

Kang Zhou, Jing Li, Weixin Luo +6

Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. Most existing methods solve this problem with a sel…

cs.CV2021

VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection

Yujun Zhang, Lei Zhu, Wei Feng +5

Lane detection plays a key role in autonomous driving. While car cameras always take streaming videos on the way, current lane detection works mainly focus on individual images (fr…

cs.CV20215 cited

From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data

Ye Liu, Lei Zhu, Shunda Pei +5

Single image dehazing is a challenging task, for which the domain shift between synthetic training data and real-world testing images usually leads to degradation of existing metho…

cs.CV2021

Few-Shot Domain Adaptation with Polymorphic Transformers

Shaohua Li, Xiuchao Sui, Jie Fu +7

Deep neural networks (DNNs) trained on one set of medical images often experience severe performance drop on unseen test images, due to various domain discrepancy between the train…

eess.IV20211 cited

DONet: Dual-Octave Network for Fast MR Image Reconstruction

Chun-Mei Feng, Zhanyuan Yang, Huazhu Fu +3

Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration has long been the subject of research. This is commonly achieved by obtaining multi…

cs.CV202110 cited

A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation

Yinglin Zhang, Risa Higashita, Huazhu Fu +5

Corneal endothelial cell segmentation plays a vital role inquantifying clinical indicators such as cell density, coefficient of variation,and hexagonality. However, the corneal end…