21 citations · 42 across the 13 of their papers we have counts for
4 papers · 1 filter
Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation
Qingjie Zeng, Yutong Xie, Zilin Lu +3
Annotation scarcity has become a major obstacle for training powerful deep-learning models for medical image segmentation, restricting their deployment in clinical scenarios. To ad…
CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation
Yicheng Wu, Zhonghua Wu, Hengcan Shi +3
New lesion segmentation is essential to estimate the disease progression and therapeutic effects during multiple sclerosis (MS) clinical treatments. However, the expensive data acq…
Universal Semi-Supervised Learning for Medical Image Classification
Lie Ju, Yicheng Wu, Wei Feng +4
Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning met…
Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation
Zhonghua Wu, Yicheng Wu, Guosheng Lin +1
Weakly-supervised point cloud segmentation with extremely limited labels is highly desirable to alleviate the expensive costs of collecting densely annotated 3D points. This paper…