10 citations · 27 across the 16 of their papers we have counts for
17 papers
RealKeyMorph: Keypoints in Real-world Coordinates for Resolution-agnostic Image Registration
Mina C. Moghadam, Alan Q. Wang, Omer Taub +2
Many real-world settings require registration of a pair of medical images that differ in spatial resolution, which may arise from differences in image acquisition parameters like p…
Adapting to Shifting Correlations with Unlabeled Data Calibration
Minh Nguyen, Alan Q. Wang, Heejong Kim +1
Distribution shifts between sites can seriously degrade model performance since models are prone to exploiting unstable correlations. Thus, many methods try to find features that a…
Knockout: A simple way to handle missing inputs
Minh Nguyen, Batuhan K. Karaman, Heejong Kim +3
Deep learning models benefit from rich (e.g., multi-modal) input features. However, multimodal models might be challenging to deploy, because some inputs may be missing at inferenc…
BrainMorph: A Foundational Keypoint Model for Robust and Flexible Brain MRI Registration
Alan Q. Wang, Rachit Saluja, Heejong Kim +3
We present a keypoint-based foundation model for general purpose brain MRI registration, based on the recently-proposed KeyMorph framework. Our model, called BrainMorph, serves as…
Robust Learning via Conditional Prevalence Adjustment
Minh Nguyen, Alan Q. Wang, Heejong Kim +1
Healthcare data often come from multiple sites in which the correlations between confounding variables can vary widely. If deep learning models exploit these unstable correlations,…
A Framework for Interpretability in Machine Learning for Medical Imaging
Alan Q. Wang, Batuhan K. Karaman, Heejong Kim +4
Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretabilit…
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
Alan Q. Wang, Minh Nguyen, Mert R. Sabuncu
Machine learning models will often fail when deployed in an environment with a data distribution that is different than the training distribution. When multiple environments are av…
A Robust and Interpretable Deep Learning Framework for Multi-modal Registration via Keypoints
Alan Q. Wang, Evan M. Yu, Adrian V. Dalca +1
We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for r…
Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processing
Xinzi He, Alan Wang, Mert R. Sabuncu
Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space. In this paper, we propose an end-to-end weak…
A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification
Alan Q. Wang, Mert R. Sabuncu
In this paper, we empirically analyze a simple, non-learnable, and nonparametric Nadaraya-Watson (NW) prediction head that can be used with any neural network architecture. In the…
Hyper-Convolutions via Implicit Kernels for Medical Imaging
Tianyu Ma, Alan Q. Wang, Adrian V. Dalca +1
The convolutional neural network (CNN) is one of the most commonly used architectures for computer vision tasks. The key building block of a CNN is the convolutional kernel that ag…
Computing Multiple Image Reconstructions with a Single Hypernetwork
Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu
Deep learning based techniques achieve state-of-the-art results in a wide range of image reconstruction tasks like compressed sensing. These methods almost always have hyperparamet…
Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy
Alan Q. Wang, Aaron K. LaViolette, Leo Moon +2
Compressed sensing fluorescence microscopy (CS-FM) proposes a scheme whereby less measurements are collected during sensing and reconstruction is performed to recover the image. Mu…
Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks
Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu
Reconstructing under-sampled k-space measurements in Compressed Sensing MRI (CS-MRI) is classically solved with regularized least-squares. Recently, deep learning has been used to…
Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data
Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu
Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing under-sampled MR images, offering the potential to reduce scan times. Classical techniques minimize a regularize…
Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI
Jinwei Zhang, Hang Zhang, Alan Wang +5
The previously established LOUPE (Learning-based Optimization of the Under-sampling Pattern) framework for optimizing the k-space sampling pattern in MRI was extended in three fold…
4DFlowNet: Super-Resolution 4D Flow MRI using Deep Learning and Computational Fluid Dynamics
Edward Ferdian, Avan Suinesiaputra, David Dubowitz +4
4D-flow magnetic resonance imaging (MRI) is an emerging imaging technique where spatiotemporal 3D blood velocity can be captured with full volumetric coverage in a single non-invas…