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07/21/2023
Generating Image-Specific Text Improves Fine-grained Image Classification
Recent vision-language models outperform vision-only models on many imag...
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07/20/2023
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series
Self-supervised learning (SSL) for clinical time series data has receive...
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07/06/2023
Multi-Similarity Contrastive Learning
Given a similarity metric, contrastive methods learn a representation in...
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04/18/2023
Coarse race data conceals disparities in clinical risk score performance
Healthcare data in the United States often records only a patient's coar...
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04/15/2023
Non-Proportional Parametrizations for Stable Hypernetwork Learning
Hypernetworks are neural networks that generate the parameters of anothe...
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04/12/2023
UniverSeg: Universal Medical Image Segmentation
While deep learning models have become the predominant method for medica...
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04/11/2023
Amortized Learning of Dynamic Feature Scaling for Image Segmentation
Convolutional neural networks (CNN) have become the predominant model fo...
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11/05/2022
SizeGAN: Improving Size Representation in Clothing Catalogs
Online clothing catalogs lack diversity in body shape and garment size. ...
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07/09/2022
At the Intersection of Deep Learning and Conceptual Art: The End of Signature
MIT wanted to commission a large scale artwork that would serve to 'illu...
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06/27/2022
Improved Text Classification via Test-Time Augmentation
Test-time augmentation – the aggregation of predictions across transform...
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04/09/2022
Data Augmentation for Electrocardiograms
Neural network models have demonstrated impressive performance in predic...
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03/30/2022
Learning the Effect of Registration Hyperparameters with HyperMorph
We introduce HyperMorph, a framework that facilitates efficient hyperpar...
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06/21/2021
Multiplying Matrices Without Multiplying
Multiplying matrices is among the most fundamental and compute-intensive...
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03/04/2021
Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction
The impact of machine learning models on healthcare will depend on the d...
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01/04/2021
HyperMorph: Amortized Hyperparameter Learning for Image Registration
We present HyperMorph, a learning-based strategy for deformable image re...
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11/23/2020
When and Why Test-Time Augmentation Works
Test-time augmentation (TTA)—the aggregation of predictions across trans...
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07/20/2020
Unsupervised Domain Adaptation in the Absence of Source Data
Current unsupervised domain adaptation methods can address many types of...
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05/29/2020
Anatomical Predictions using Subject-Specific Medical Data
Changes over time in brain anatomy can provide important insight for tre...
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03/06/2020
What is the State of Neural Network Pruning?
Neural network pruning—the task of reducing the size of a network by rem...
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11/30/2019
Image segmentation of liver stage malaria infection with spatial uncertainty sampling
Global eradication of malaria depends on the development of drugs effect...
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10/10/2019
Estimation of Utility-Maximizing Bounds on Potential Outcomes
Estimation of individual treatment effects is often used as the basis fo...
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08/07/2019
Learning Conditional Deformable Templates with Convolutional Networks
We develop a learning framework for building deformable templates, which...
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03/08/2019
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Classical deformable registration techniques achieve impressive results ...
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03/08/2019
Unsupervised Data Imputation via Variational Inference of Deep Subspaces
A wide range of systems exhibit high dimensional incomplete data. Accura...
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03/07/2019
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
We consider the problem of segmenting a biomedical image into anatomical...
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12/17/2018
Fast Learning-based Registration of Sparse Clinical Images
Deformable registration of clinical scans is a fundamental task for many...
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12/02/2018
Multiple Instance Learning for ECG Risk Stratification
In this paper, we apply a multiple instance learning paradigm to signal-...
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09/14/2018
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
We present VoxelMorph, a fast, unsupervised, learning-based algorithm fo...
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08/07/2018
Sprintz: Time Series Compression for the Internet of Things
Thanks to the rapid proliferation of connected devices, sensor-generated...
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06/07/2018
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
Machine learning approaches have been effective in predicting adverse ou...
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05/11/2018
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
Traditional deformable registration techniques achieve impressive result...
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04/20/2018
Synthesizing Images of Humans in Unseen Poses
We address the computational problem of novel human pose synthesis. Give...
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02/07/2018
An Unsupervised Learning Model for Deformable Medical Image Registration
We present an efficient learning-based algorithm for deformable, pairwis...
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12/13/2016
