activity
20192021
most citedA relic sketch extraction framework based on detail-aware hierarchical deep network

15 citations · 15 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CV202115 cited

A relic sketch extraction framework based on detail-aware hierarchical deep network

Jinye Peng, Jiaxin Wang, Jun Wang +5

As the first step of the restoration process of painted relics, sketch extraction plays an important role in cultural research. However, sketch extraction suffers from serious dise…

cs.CV2020

A Matlab Toolbox for Feature Importance Ranking

Shaode Yu, Zhicheng Zhang, Xiaokun Liang +4

More attention is being paid for feature importance ranking (FIR), in particular when thousands of features can be extracted for intelligent diagnosis and personalized medicine. A…

eess.IV2020

Robustness study of noisy annotation in deep learning based medical image segmentation

Shaode Yu, Erlei Zhang, Junjie Wu +6

Partly due to the use of exhaustive-annotated data, deep networks have achieved impressive performance on medical image segmentation. Medical imaging data paired with noisy annotat…

eess.IV2019

Breast Ultrasound Computer-Aided Diagnosis Using Structure-Aware Triplet Path Networks

Erlei Zhang, Zi Yang, Stephen Seiler +3

Breast ultrasound (US) is an effective imaging modality for breast cancer detec-tion and diagnosis. The structural characteristics of breast lesion play an im-portant role in Compu…

physics.med-ph2019

BIRADS Features-Oriented Semi-supervised Deep Learning for Breast Ultrasound Computer-Aided Diagnosis

Erlei Zhang, Stephen Seiler, Mingli Chen +2

Breast ultrasound (US) is an effective imaging modality for breast cancer detection and diagnosis. US computer-aided diagnosis (CAD) systems have been developed for decades and hav…