85 citations · 136 across the 33 of their papers we have counts for
6 papers · 1 filter
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging
Peirong Liu, Oula Puonti, Xiaoling Hu +2
Recent learning-based approaches have made astonishing advances in calibrated medical imaging like computerized tomography (CT), yet they struggle to generalize in uncalibrated mod…
Semi-supervised Segmentation of Histopathology Images with Noise-Aware Topological Consistency
Meilong Xu, Xiaoling Hu, Saumya Gupta +2
In digital pathology, segmenting densely distributed objects like glands and nuclei is crucial for downstream analysis. Since detailed pixel-wise annotations are very time-consumin…
Calibrating Uncertainty for Semi-Supervised Crowd Counting
Chen Li, Xiaoling Hu, Shahira Abousamra +1
Semi-supervised crowd counting is an important yet challenging task. A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set…
Confidence Estimation Using Unlabeled Data
Chen Li, Xiaoling Hu, Chao Chen
Overconfidence is a common issue for deep neural networks, limiting their deployment in real-world applications. To better estimate confidence, existing methods mostly focus on ful…
Topology-Aware Uncertainty for Image Segmentation
Saumya Gupta, Yikai Zhang, Xiaoling Hu +2
Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology. To facilitate and accelera…
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
Aishik Konwer, Xiaoling Hu, Joseph Bae +3
In medical vision, different imaging modalities provide complementary information. However, in practice, not all modalities may be available during inference or even training. Prev…