1 citations · 3 across the 4 of their papers we have counts for
7 papers
Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation
Hexin Dong, Zifan Chen, Mingze Yuan +5
As one of the most challenging and practical segmentation tasks, open-world semantic segmentation requires the model to segment the anomaly regions in the images and incrementally…
BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation
Mo Zhang, Fei Yu, Jie Zhao +2
Blood vessel segmentation is crucial for many diagnostic and research applications. In recent years, CNN-based models have leaded to breakthroughs in the task of segmentation, howe…
PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation
Jie Zhao, Lei Dai, Mo Zhang +5
Automated cervical nucleus segmentation based on deep learning can effectively improve the quantitative analysis of cervical cancer. However, accurate nuclei segmentation is still…
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images
Fei Yu, Jie Zhao, Yanjun Gong +6
Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual ann…
ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning
Mo Zhang, Jie Zhao, Xiang Li +2
Extracting multi-scale information is key to semantic segmentation. However, the classic convolutional neural networks (CNNs) encounter difficulties in achieving multi-scale inform…
Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks
Wenjia Wang, Junxuan Chen, Jie Zhao +4
The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segme…