416 citations · 748 across the 56 of their papers we have counts for
11 papers · 1 filter
Discovering Hidden Physics Behind Transport Dynamics
Peirong Liu, Lin Tian, Yubo Zhang +3
Transport processes are ubiquitous. They are, for example, at the heart of optical flow approaches; or of perfusion imaging, where blood transport is assessed, most commonly by inj…
VoteNet++: Registration Refinement for Multi-Atlas Segmentation
Zhipeng Ding, Marc Niethammer
Multi-atlas segmentation (MAS) is a popular image segmentation technique for medical images. In this work, we improve the performance of MAS by correcting registration errors befor…
Perfusion Imaging: A Data Assimilation Approach
Peirong Liu, Yueh Z. Lee, Stephen R. Aylward +1
Perfusion imaging (PI) is clinically used to assess strokes and brain tumors. Commonly used PI approaches based on magnetic resonance imaging (MRI) or computed tomography (CT) meas…
A Deep Network for Joint Registration and Reconstruction of Images with Pathologies
Xu Han, Zhengyang Shen, Zhenlin Xu +5
Registration of images with pathologies is challenging due to tissue appearance changes and missing correspondences caused by the pathologies. Moreover, mass effects as observed fo…
The Fairness-Accuracy Pareto Front
Susan Wei, Marc Niethammer
Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring fairness often comes at the cost of accuracy. We provide…
Local Temperature Scaling for Probability Calibration
Zhipeng Ding, Xu Han, Peirong Liu +1
For semantic segmentation, label probabilities are often uncalibrated as they are typically only the by-product of a segmentation task. Intersection over Union (IoU) and Dice score…
Anatomical Data Augmentation via Fluid-based Image Registration
Zhengyang Shen, Zhenlin Xu, Sahin Olut +1
We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpo…
Robust and Generalizable Visual Representation Learning via Random Convolutions
Zhenlin Xu, Deyi Liu, Junlin Yang +2
While successful for various computer vision tasks, deep neural networks have shown to be vulnerable to texture style shifts and small perturbations to which humans are robust. In…
Deep Goal-Oriented Clustering
Yifeng Shi, Christopher M. Bender, Junier B. Oliva +1
Clustering and prediction are two primary tasks in the fields of unsupervised and supervised learning, respectively. Although much of the recent advances in machine learning have b…
A Shooting Formulation of Deep Learning
François-Xavier Vialard, Roland Kwitt, Susan Wei +1
Continuous-depth neural networks can be viewed as deep limits of discrete neural networks whose dynamics resemble a discretization of an ordinary differential equation (ODE). Altho…
Topologically Densified Distributions
Christoph D. Hofer, Florian Graf, Marc Niethammer +1
We study regularization in the context of small sample-size learning with over-parameterized neural networks. Specifically, we shift focus from architectural properties, such as no…