2.2k citations · 4.4k across the 41 of their papers we have counts for
47 papers
The 802.11 MAC protocol leads to inefficient equilibria
Godfrey Tan, John Guttag
Wireless local area networks (WLANs) based on the family of 802.11 technologies are becoming ubiquitous. These technologies support multiple data transmission rates. Transmitting a…
Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains
Marianne Rakic, Siyu Gai, Etienne Chollet +2
A single biomedical image can be meaningfully segmented in multiple ways, depending on the desired application. For instance, a brain MRI can be segmented according to tissue types…
AtlasMorph: Learning conditional deformable templates for brain MRI
Marianne Rakic, Andrew Hoopes, S. Mazdak Abulnaga +3
Deformable templates, or atlases, are images that represent a prototypical anatomy for a population, and are often enhanced with probabilistic anatomical label maps. They are commo…
VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysis
Andrew Hoopes, Neel Dey, Victor Ion Butoi +2
We present VoxelPrompt, an end-to-end image analysis agent that tackles free-form radiological tasks. Given any number of volumetric medical images and a natural language prompt, V…
UniverSeg: Universal Medical Image Segmentation
Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma +3
While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving…
Coarse race data conceals disparities in clinical risk score performance
Rajiv Movva, Divya Shanmugam, Kaihua Hou +4
Healthcare data in the United States often records only a patient's coarse race group: for example, both Indian and Chinese patients are typically coded as "Asian." It is unknown,…
Magnitude Invariant Parametrizations Improve Hypernetwork Learning
Jose Javier Gonzalez Ortiz, John Guttag, Adrian Dalca
Hypernetworks, neural networks that predict the parameters of another neural network, are powerful models that have been successfully used in diverse applications from image genera…
Scale-Space Hypernetworks for Efficient Biomedical Imaging
Jose Javier Gonzalez Ortiz, John Guttag, Adrian Dalca
Convolutional Neural Networks (CNNs) are the predominant model used for a variety of medical image analysis tasks. At inference time, these models are computationally intensive, es…
SizeGAN: Improving Size Representation in Clothing Catalogs
Kathleen M. Lewis, John Guttag
Online clothing catalogs lack diversity in body shape and garment size. Brands commonly display their garments on models of one or two sizes, rarely including plus-size models. To…
At the Intersection of Deep Learning and Conceptual Art: The End of Signature
Divya Shanmugam, Katie Lewis, Jose Javier Gonzalez-Ortiz +2
MIT wanted to commission a large scale artwork that would serve to 'illuminate a new campus gateway, inaugurate a space of exchange between MIT and Cambridge, and inspire our stude…
Improved Text Classification via Test-Time Augmentation
Helen Lu, Divya Shanmugam, Harini Suresh +1
Test-time augmentation -- the aggregation of predictions across transformed examples of test inputs -- is an established technique to improve the performance of image classificatio…
Saliency Cards: A Framework to Characterize and Compare Saliency Methods
Angie Boggust, Harini Suresh, Hendrik Strobelt +2
Saliency methods are a common class of machine learning interpretability techniques that calculate how important each input feature is to a model's output. We find that, with the r…
Data Augmentation for Electrocardiograms
Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev +2
Neural network models have demonstrated impressive performance in predicting pathologies and outcomes from the 12-lead electrocardiogram (ECG). However, these models often need to…
Learning the Effect of Registration Hyperparameters with HyperMorph
Andrew Hoopes, Malte Hoffmann, Douglas N. Greve +3
We introduce HyperMorph, a framework that facilitates efficient hyperparameter tuning in learning-based deformable image registration. Classical registration algorithms perform an…
Multiplying Matrices Without Multiplying
Davis Blalock, John Guttag
Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning. Consequently, there has been significant work on efficiently approximating…
Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction
Aniruddh Raghu, John Guttag, Katherine Young +3
The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. In this paper, w…
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs
Harini Suresh, Kathleen M. Lewis, John V. Guttag +1
Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models. However, existing approaches often rely on abstract, complex v…
HyperMorph: Amortized Hyperparameter Learning for Image Registration
Andrew Hoopes, Malte Hoffmann, Bruce Fischl +2
We present HyperMorph, a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training. Classical r…
Better Aggregation in Test-Time Augmentation
Divya Shanmugam, Davis Blalock, Guha Balakrishnan +1
Test-time augmentation -- the aggregation of predictions across transformed versions of a test input -- is a common practice in image classification. Traditionally, predictions are…
Unsupervised Domain Adaptation in the Absence of Source Data
Roshni Sahoo, Divya Shanmugam, John Guttag
Current unsupervised domain adaptation methods can address many types of distribution shift, but they assume data from the source domain is freely available. As the use of pre-trai…
Anatomical Predictions using Subject-Specific Medical Data
Marianne Rakic, John Guttag, Adrian V. Dalca
Changes over time in brain anatomy can provide important insight for treatment design or scientific analyses. We present a method that predicts how a brain MRI for an individual wi…
What is the State of Neural Network Pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle +1
Neural network pruning---the task of reducing the size of a network by removing parameters---has been the subject of a great deal of work in recent years. We provide a meta-analysi…
Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings
Amy Zhao, Guha Balakrishnan, Kathleen M. Lewis +3
We introduce a new video synthesis task: synthesizing time lapse videos depicting how a given painting might have been created. Artists paint using unique combinations of brushes,…
Image segmentation of liver stage malaria infection with spatial uncertainty sampling
Ava P. Soleimany, Harini Suresh, Jose Javier Gonzalez Ortiz +4
Global eradication of malaria depends on the development of drugs effective against the silent, yet obligate liver stage of the disease. The gold standard in drug development remai…