11 citations · 30 across the 6 of their papers we have counts for
7 papers · 1 filter
Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation
Yao Zhang, Jiawei Yang, Jiang Tian +4
Liver cancer is one of the most common cancers worldwide. Due to inconspicuous texture changes of liver tumor, contrast-enhanced computed tomography (CT) imaging is effective for t…
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities
Yixin Wang, Yang Zhang, Yang Liu +6
Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is clinically relevant in diagnoses, prognoses and surgery treatment, which requires multiple modalities…
Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer
Yao Zhang, Jiawei Yang, Feng Hou +6
Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice. However,…
Modality-Pairing Learning for Brain Tumor Segmentation
Yixin Wang, Yao Zhang, Feng Hou +5
Automatic brain tumor segmentation from multi-modality Magnetic Resonance Images (MRI) using deep learning methods plays an important role in assisting the diagnosis and treatment…
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
Nicholas Heller, Fabian Isensee, Klaus H. Maier-Hein +38
There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors a…
Semantic Feature Attention Network for Liver Tumor Segmentation in Large-scale CT database
Yao Zhang, Cheng Zhong, Yang Zhang +2
Liver tumor segmentation plays an important role in hepatocellular carcinoma diagnosis and surgical planning. In this paper, we propose a novel Semantic Feature Attention Network (…