Adrian V. Dalca

verfied profile

MIT and HMS. Machine Learning for Health and computer vision.

http://adalca.mit.edu

Hyper-Convolution Networks for Biomedical Image Segmentation

The convolution operation is a central building block of neural network ...
05/21/2021 ∙ by Tianyu Ma, et al. ∙ 0 ∙ share

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End-to-End Sequential Sampling and Reconstruction for MR Imaging

Accelerated MRI shortens acquisition time by subsampling in the measurem...
05/13/2021 ∙ by Tianwei Yin, et al. ∙ 19 ∙ share

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Generative Adversarial Registration for Improved Conditional Deformable Templates

Deformable templates are essential to large-scale medical image registra...
05/07/2021 ∙ by Neel Dey, et al. ∙ 5 ∙ share

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Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction

The impact of machine learning models on healthcare will depend on the d...
03/04/2021 ∙ by Aniruddh Raghu, et al. ∙ 27 ∙ share

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Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks

Reconstructing under-sampled k-space measurements in Compressed Sensing ...
01/06/2021 ∙ by Alan Q. Wang, et al. ∙ 1 ∙ share

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HyperMorph: Amortized Hyperparameter Learning for Image Registration

We present HyperMorph, a learning-based strategy for deformable image re...
01/04/2021 ∙ by Andrew Hoopes, et al. ∙ 13 ∙ share

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Predictive Modeling of Anatomy with Genetic and Clinical Data

We present a semi-parametric generative model for predicting anatomy of ...
10/09/2020 ∙ by Adrian V. Dalca, et al. ∙ 8 ∙ share

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Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data

Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing unde...
07/29/2020 ∙ by Alan Q. Wang, et al. ∙ 16 ∙ share

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Anatomical Predictions using Subject-Specific Medical Data

Changes over time in brain anatomy can provide important insight for tre...
05/29/2020 ∙ by Marianne Rakic, et al. ∙ 9 ∙ share

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An Auto-Encoder Strategy for Adaptive Image Segmentation

Deep neural networks are powerful tools for biomedical image segmentatio...
04/29/2020 ∙ by Evan M. Yu, et al. ∙ 8 ∙ share

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Learning Multi-Modal Image Registration without Real Data

We introduce a learning-based strategy for multi-modal registration of i...
04/21/2020 ∙ by Malte Hoffmann, et al. ∙ 7 ∙ share

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Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast

Partial voluming (PV) is arguably the last crucial unsolved problem in B...
04/21/2020 ∙ by Benjamin Billot, et al. ∙ 5 ∙ share

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Learning a Probabilistic Strategy for Computational Imaging Sensor Selection

Optimized sensing is important for computational imaging in low-resource...
03/23/2020 ∙ by He Sun, et al. ∙ 25 ∙ share

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A Learning Strategy for Contrast-agnostic MRI Segmentation

We present a deep learning strategy that enables, for the first time, co...
03/04/2020 ∙ by Benjamin Billot, et al. ∙ 8 ∙ share

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ML4H Abstract Track 2019

A collection of the accepted abstracts for the Machine Learning for Heal...
02/05/2020 ∙ by Matthew B. A. McDermott, et al. ∙ 0 ∙ share

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Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings

We introduce a new video synthesis task: synthesizing time lapse videos ...
01/04/2020 ∙ by Amy Zhao, et al. ∙ 16 ∙ share

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Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions

We introduce visual deprojection: the task of recovering an image or vid...
09/01/2019 ∙ by Guha Balakrishnan, et al. ∙ 3 ∙ share

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Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation

We tackle biomedical image segmentation in the scenario of only a few la...
08/13/2019 ∙ by Hyeon Woo Lee, et al. ∙ 7 ∙ share

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Learning Conditional Deformable Templates with Convolutional Networks

We develop a learning framework for building deformable templates, which...
08/07/2019 ∙ by Adrian V. Dalca, et al. ∙ 5 ∙ share

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Adaptive Compressed Sensing MRI with Unsupervised Learning

In compressed sensing MRI, k-space measurements are under-sampled to ach...
07/26/2019 ∙ by Cagla D. Bahadir, et al. ∙ 4 ∙ share

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Automated Image Registration Quality Assessment Utilizing Deep-learning based Ventricle Extraction in Clinical Data

Registration is a core component of many imaging pipelines. In case of c...
07/01/2019 ∙ by Florian Dubost, et al. ∙ 0 ∙ share

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Confidence Calibration for Convolutional Neural Networks Using Structured Dropout

In classification applications, we often want probabilistic predictions ...
06/23/2019 ∙ by Zhilu Zhang, et al. ∙ 16 ∙ share

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Unsupervised deep learning for Bayesian brain MRI segmentation

Probabilistic atlas priors have been commonly used to derive adaptive an...
04/25/2019 ∙ by Adrian V. Dalca, et al. ∙ 1 ∙ share

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Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces

Classical deformable registration techniques achieve impressive results ...
03/08/2019 ∙ by Adrian V. Dalca, et al. ∙ 13 ∙ share

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Unsupervised Data Imputation via Variational Inference of Deep Subspaces

A wide range of systems exhibit high dimensional incomplete data. Accura...
03/08/2019 ∙ by Adrian V. Dalca, et al. ∙ 11 ∙ share

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Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

We consider the problem of segmenting a biomedical image into anatomical...
03/07/2019 ∙ by Adrian V. Dalca, et al. ∙ 3 ∙ share

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Data augmentation using learned transforms for one-shot medical image segmentation

Biomedical image segmentation is an important task in many medical appli...
02/25/2019 ∙ by Amy Zhao, et al. ∙ 33 ∙ share

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Learning-based Optimization of the Under-sampling Pattern in MRI

Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated...
01/07/2019 ∙ by Cagla Deniz Bahadir, et al. ∙ 1 ∙ share

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Fast Learning-based Registration of Sparse Clinical Images

Deformable registration of clinical scans is a fundamental task for many...
12/17/2018 ∙ by Kathleen M. Lewis, et al. ∙ 1 ∙ share

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Gaussian Process Prior Variational Autoencoders

Variational autoencoders (VAE) are a powerful and widely-used class of m...
10/28/2018 ∙ by Francesco Paolo Casale, et al. ∙ 1 ∙ share

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VoxelMorph: A Learning Framework for Deformable Medical Image Registration

We present VoxelMorph, a fast, unsupervised, learning-based algorithm fo...
09/14/2018 ∙ by Guha Balakrishnan, et al. ∙ 1 ∙ share

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Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

We propose a new iterative segmentation model which can be accurately le...
09/11/2018 ∙ by Danielle F. Pace, et al. ∙ 1 ∙ share

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Medical Image Imputation from Image Collections

We present an algorithm for creating high resolution anatomically plausi...
08/17/2018 ∙ by Adrian V. Dalca, et al. ∙ 1 ∙ share

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Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration

Traditional deformable registration techniques achieve impressive result...
05/11/2018 ∙ by Adrian V. Dalca, et al. ∙ 1 ∙ share

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Synthesizing Images of Humans in Unseen Poses

We address the computational problem of novel human pose synthesis. Give...
04/20/2018 ∙ by Guha Balakrishnan, et al. ∙ 1 ∙ share

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An Unsupervised Learning Model for Deformable Medical Image Registration

We present an efficient learning-based algorithm for deformable, pairwis...
02/07/2018 ∙ by Guha Balakrishnan, et al. ∙ 1 ∙ share

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Reconstructing Video from Interferometric Measurements of Time-Varying Sources

Very long baseline interferometry (VLBI) makes it possible to recover im...
11/03/2017 ∙ by Katherine L. Bouman, et al. ∙ 1 ∙ share

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Adrian V. Dalca

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