139 citations · 297 across the 16 of their papers we have counts for
20 papers · 1 filter
Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation
Qingjie Zeng, Yutong Xie, Zilin Lu +2
Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data especially on the task of volumetric medical image segmentation. Unlike p…
Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training
Xiaoyu Bai, Benteng Ma, Changyang Li +1
Universal lesion detection has great value for clinical practice as it aims to detect various types of lesions in multiple organs on medical images. Deep learning methods have show…
An End-to-End Framework For Universal Lesion Detection With Missing Annotations
Xiaoyu Bai, Yong Xia
Fully annotated large-scale medical image datasets are highly valuable. However, because labeling medical images is tedious and requires specialized knowledge, the large-scale data…
ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation
Shishuai Hu, Zehui Liao, Yong Xia
The domain discrepancy existed between medical images acquired in different situations renders a major hurdle in deploying pre-trained medical image segmentation models for clinica…
Learning from partially labeled data for multi-organ and tumor segmentation
Yutong Xie, Jianpeng Zhang, Yong Xia +1
Medical image benchmarks for the segmentation of organs and tumors suffer from the partially labeling issue due to its intensive cost of labor and expertise. Current mainstream app…
PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images
Haozhe Jia, Haoteng Tang, Guixiang Ma +4
Automated and accurate segmentation of the infected regions in computed tomography (CT) images is critical for the prediction of the pathological stage and treatment response of CO…