activity
20192022
most citedCross-Modality Deep Feature Learning for Brain Tumor Segmentation

290 citations · 362 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2022

Domain Invariant Model with Graph Convolutional Network for Mammogram Classification

Churan Wang, Jing Li, Xinwei Sun +3

Due to its safety-critical property, the image-based diagnosis is desired to achieve robustness on out-of-distribution (OOD) samples. A natural way towards this goal is capturing o…

eess.IV2022

Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis

Yunlong Zhang, Xin Lin, Yihong Zhuang +6

Synthesizing a subject-specific pathology-free image from a pathological image is valuable for algorithm development and clinical practice. In recent years, several approaches base…

eess.IV2022290 cited

Cross-Modality Deep Feature Learning for Brain Tumor Segmentation

Dingwen Zhang, Guohai Huang, Qiang Zhang +3

Recent advances in machine learning and prevalence of digital medical images have opened up an opportunity to address the challenging brain tumor segmentation (BTS) task by using d…

eess.IV202140 cited

Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images

Kongming Liang, Kai Han, Xiuli Li +4

Quantitative estimation of the acute ischemic infarct is crucial to improve neurological outcomes of the patients with stroke symptoms. Since the density of lesions is subtle and c…

cs.AI202124 cited

Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System

Gang Yu, Zhongzhi Yu, Yemin Shi +7

Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract infection (RTI), are among the most common diseases in clinics. The similarities among th…

cs.CV2019

EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection

Xiao Song, Xu Zhao, Liangji Fang +1

Recently, leveraging on the development of end-to-end convolutional neural networks (CNNs), deep stereo matching networks have achieved remarkable performance far exceeding traditi…