416 citations · 748 across the 56 of their papers we have counts for
11 papers · 1 filter
Joint and individual analysis of breast cancer histologic images and genomic covariates
Iain Carmichael, Benjamin C. Calhoun, Katherine A. Hoadley +8
A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breas…
VoteNet+ : An Improved Deep Learning Label Fusion Method for Multi-atlas Segmentation
Zhipeng Ding, Xu Han, Marc Niethammer
In this work, we improve the performance of multi-atlas segmentation (MAS) by integrating the recently proposed VoteNet model with the joint label fusion (JLF) approach. Specifical…
Deep Message Passing on Sets
Yifeng Shi, Junier Oliva, Marc Niethammer
Modern methods for learning over graph input data have shown the fruitfulness of accounting for relationships among elements in a collection. However, most methods that learn over…
Deep Multi-View Learning via Task-Optimal CCA
Heather D. Couture, Roland Kwitt, J. S. Marron +3
Canonical Correlation Analysis (CCA) is widely used for multimodal data analysis and, more recently, for discriminative tasks such as multi-view learning; however, it makes no use…
Connectivity-Optimized Representation Learning via Persistent Homology
Christoph Hofer, Roland Kwitt, Mandar Dixit +1
We study the problem of learning representations with controllable connectivity properties. This is beneficial in situations when the imposed structure can be leveraged upstream. I…
Region-specific Diffeomorphic Metric Mapping
Zhengyang Shen, François-Xavier Vialard, Marc Niethammer
We introduce a region-specific diffeomorphic metric mapping (RDMM) registration approach. RDMM is non-parametric, estimating spatio-temporal velocity fields which parameterize the…
VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation
Zhipeng Ding, Xu Han, Marc Niethammer
Deep learning (DL) approaches are state-of-the-art for many medical image segmentation tasks. They offer a number of advantages: they can be trained for specific tasks, computation…
Graph Filtration Learning
Christoph D. Hofer, Florian Graf, Bastian Rieck +2
We propose an approach to learning with graph-structured data in the problem domain of graph classification. In particular, we present a novel type of readout operation to aggregat…
Metric Learning for Image Registration
Marc Niethammer, Roland Kwitt, Francois-Xavier Vialard
Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. Howe…
DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation
Zhenlin Xu, Marc Niethammer
Deep convolutional neural networks (CNNs) are state-of-the-art for semantic image segmentation, but typically require many labeled training samples. Obtaining 3D segmentations of m…
Networks for Joint Affine and Non-parametric Image Registration
Zhengyang Shen, Xu Han, Zhenlin Xu +1
We introduce an end-to-end deep-learning framework for 3D medical image registration. In contrast to existing approaches, our framework combines two registration methods: an affine…