most citedPGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation

1 citations · 1 across the 1 of their papers we have counts for

collaborators

3 papers

eess.IV2019★ 1 cited

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…

cs.CV2019

Multi-level Domain Adaptive learning for Cross-Domain Detection

Rongchang Xie, Fei Yu, Jiachao Wang +2

In recent years, object detection has shown impressive results using supervised deep learning, but it remains challenging in a cross-domain environment. The variations of illuminat…

eess.IV2019

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…