Search Results for author:
Found 18 papers, 7 papers with code
Registration by Regression (RbR): a framework for interpretable and flexible atlas registration
In human neuroimaging studies, atlas registration enables mapping MRI scans to a common coordinate frame, which is necessary to aggregate data from multiple subjects.
Learning Topological Representations for Deep Image Understanding
In many scenarios, especially biomedical applications, the correct delineation of complex fine-scaled structures such as neurons, tissues, and vessels is critical for downstream analysis.
P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI
White matter hyperintensities (WMH) are a hallmark of cerebrovascular disease and multiple sclerosis.
TopoSemiSeg: Enforcing Topological Consistency for Semi-Supervised Segmentation of Histopathology Images
To address this issue, we propose TopoSemiSeg, the first semi-supervised method that learns the topological representation from unlabeled data.
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging
We present new metrics to validate the intra- and inter-subject robustness of Brain-ID features, and evaluate their performance on four downstream applications, covering contrast-independent (anatomy reconstruction/contrast synthesis, brain segmentation), and contrast-dependent (super-resolution, bias field estimation) tasks.
Calibrating Uncertainty for Semi-Supervised Crowd Counting
A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set.
Confidence Estimation Using Unlabeled Data
We stipulate that even with limited training labels, we can still reasonably approximate the confidence of model on unlabeled samples by inspecting the prediction consistency through the training process.
Topology-Aware Uncertainty for Image Segmentation
Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology.
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
We propose a novel approach to learn enhanced modality-agnostic representations by employing a meta-learning strategy in training, even when only limited full modality samples are available.
Deep Statistic Shape Model for Myocardium Segmentation
Additionally, the predicted point cloud guarantees boundary correspondence for sequential images, which contributes to the downstream tasks, such as the motion estimation of myocardium.
Learning Topological Interactions for Multi-Class Medical Image Segmentation
In this paper, we introduce a novel topological interaction module to encode the topological interactions into a deep neural network.
Learning Probabilistic Topological Representations Using Discrete Morse Theory
We use discrete Morse theory and persistent homology to construct an one-parameter family of structures as the topological/structural representation space.
A Manifold View of Adversarial Risk
Assuming data lies in a manifold, we investigate two new types of adversarial risk, the normal adversarial risk due to perturbation along normal direction, and the in-manifold adversarial risk due to perturbation within the manifold.
A Topology-Attention ConvLSTM Network and Its Application to EM Images
We propose a novel TopologyAttention ConvLSTM Network (TACNet) for 3D image segmentation in order to achieve high structural accuracy for 3D segmentation tasks.
Structure-Aware Image Segmentation with Homotopy Warping
By focusing on these critical pixels, we propose a new homotopy warping loss to train deep image segmentation networks for better topological accuracy.
Trigger Hunting with a Topological Prior for Trojan Detection
Despite their success and popularity, deep neural networks (DNNs) are vulnerable when facing backdoor attacks.
Topology-Aware Segmentation Using Discrete Morse Theory
In the segmentation of fine-scale structures from natural and biomedical images, per-pixel accuracy is not the only metric of concern.
Topology-Preserving Deep Image Segmentation
Segmentation algorithms are prone to make topological errors on fine-scale structures, e. g., broken connections.
