416 citations · 748 across the 56 of their papers we have counts for
61 papers
Self-supervised Landmark Learning with Deformation Reconstruction and Cross-subject Consistency Objectives
Chun-Hung Chao, Marc Niethammer
A Point Distribution Model (PDM) is the basis of a Statistical Shape Model (SSM) that relies on a set of landmark points to represent a shape and characterize the shape variation.…
Multimodal Understanding Through Correlation Maximization and Minimization
Yifeng Shi, Marc Niethammer
Multimodal learning has mainly focused on learning large models on, and fusing feature representations from, different modalities for better performances on downstream tasks. In th…
Inverse Consistency by Construction for Multistep Deep Registration
Hastings Greer, Lin Tian, Francois-Xavier Vialard +5
Inverse consistency is a desirable property for image registration. We propose a simple technique to make a neural registration network inverse consistent by construction, as a con…
Unsupervised Discovery of 3D Hierarchical Structure with Generative Diffusion Features
Nurislam Tursynbek, Marc Niethammer
Inspired by recent findings that generative diffusion models learn semantically meaningful representations, we use them to discover the intrinsic hierarchical structure in biomedic…
MRIS: A Multi-modal Retrieval Approach for Image Synthesis on Diverse Modalities
Boqi Chen, Marc Niethammer
Multiple imaging modalities are often used for disease diagnosis, prediction, or population-based analyses. However, not all modalities might be available due to cost, different st…
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
Yining Jiao, Carlton Zdanski, Julia Kimbell +8
Deep implicit functions (DIFs) have emerged as a powerful paradigm for many computer vision tasks such as 3D shape reconstruction, generation, registration, completion, editing, an…
Exploring Cycle Consistency Learning in Interactive Volume Segmentation
Qin Liu, Meng Zheng, Benjamin Planche +4
Automatic medical volume segmentation often lacks clinical accuracy, necessitating further refinement. In this work, we interactively approach medical volume segmentation via two d…
Harmonization Benchmarking Tool for Neuroimaging Datasets
Tom Osika, Ebrahim Ebrahim, Martin Styner +3
A major data pre-processing step for large, multi-site studies is to handle site effects by harmonizing data, generating a dataset that enables more powerful analyses and more robu…
Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language
Zhenlin Xu, Marc Niethammer, Colin Raffel
Deep learning models struggle with compositional generalization, i.e. the ability to recognize or generate novel combinations of observed elementary concepts. In hopes of enabling…
SimpleClick: Interactive Image Segmentation with Simple Vision Transformers
Qin Liu, Zhenlin Xu, Gedas Bertasius +1
Click-based interactive image segmentation aims at extracting objects with a limited user clicking. A hierarchical backbone is the de-facto architecture for current methods. Recent…
Optimal Transport Features for Morphometric Population Analysis
Samuel Gerber, Marc Niethammer, Ebrahim Ebrahim +5
Brain pathologies often manifest as partial or complete loss of tissue. The goal of many neuroimaging studies is to capture the location and amount of tissue changes with respect t…
PseudoClick: Interactive Image Segmentation with Click Imitation
Qin Liu, Meng Zheng, Benjamin Planche +4
The goal of click-based interactive image segmentation is to obtain precise object segmentation masks with limited user interaction, i.e., by a minimal number of user clicks. Exist…
: Approximate Diffeomorphisms via Gradient Inverse Consistency
Lin Tian, Hastings Greer, François-Xavier Vialard +6
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration tec…
Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning with Pairwise Alignment
Zhipeng Ding, Marc Niethammer
Atlas building and image registration are important tasks for medical image analysis. Once one or multiple atlases from an image population have been constructed, commonly (1) imag…
Fluid registration between lung CT and stationary chest tomosynthesis images
Lin Tian, Connor Puett, Peirong Liu +4
Registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondences between organs of interest between planning and treatment images. H…
LiftReg: Limited Angle 2D/3D Deformable Registration
Lin Tian, Yueh Z. Lee, Raúl San José Estépar +1
We propose LiftReg, a 2D/3D deformable registration approach. LiftReg is a deep registration framework which is trained using sets of digitally reconstructed radiographs (DRR) and…
On Measuring Excess Capacity in Neural Networks
Florian Graf, Sebastian Zeng, Bastian Rieck +2
We study the excess capacity of deep networks in the context of supervised classification. That is, given a capacity measure of the underlying hypothesis class - in our case, empir…
iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images
Qin Liu, Zhenlin Xu, Yining Jiao +1
We propose iSegFormer, a memory-efficient transformer that combines a Swin transformer with a lightweight multilayer perceptron (MLP) decoder. With the efficient Swin transformer b…
Deep Decomposition for Stochastic Normal-Abnormal Transport
Peirong Liu, Yueh Lee, Stephen Aylward +1
Advection-diffusion equations describe a large family of natural transport processes, e.g., fluid flow, heat transfer, and wind transport. They are also used for optical flow and p…
Accurate Point Cloud Registration with Robust Optimal Transport
Zhengyang Shen, Jean Feydy, Peirong Liu +4
This work investigates the use of robust optimal transport (OT) for shape matching. Specifically, we show that recent OT solvers improve both optimization-based and deep learning m…
ICON: Learning Regular Maps Through Inverse Consistency
Hastings Greer, Roland Kwitt, Francois-Xavier Vialard +1
Learning maps between data samples is fundamental. Applications range from representation learning, image translation and generative modeling, to the estimation of spatial deformat…
Dissecting Supervised Contrastive Learning
Florian Graf, Christoph D. Hofer, Marc Niethammer +1
Minimizing cross-entropy over the softmax scores of a linear map composed with a high-capacity encoder is arguably the most popular choice for training neural networks on supervise…
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…