1 code implementation • 24 Jan 2024 • Marianne Rakic, Hallee E. Wong, Jose Javier Gonzalez Ortiz, Beth Cimini, John Guttag, Adrian V. Dalca
Existing learning-based solutions to medical image segmentation have two important shortcomings.
1 code implementation • 12 Dec 2023 • Hallee E. Wong, Marianne Rakic, John Guttag, Adrian V. Dalca
These include a training strategy that incorporates both a highly diverse set of images and tasks, novel algorithms for simulated user interactions and labels, and a network that enables fast inference.
1 code implementation • 21 Jul 2023 • Kathleen M. Lewis, Emily Mu, Adrian V. Dalca, John Guttag
We demonstrate the utility of GIST by fine-tuning vision-language models on the image-and-generated-text pairs to learn an aligned vision-language representation space for improved classification.
Fine-Grained Image Classification Image-text Classification +4
no code implementations • 20 Jul 2023 • Aniruddh Raghu, Payal Chandak, Ridwan Alam, John Guttag, Collin M. Stultz
However, most existing SSL methods for clinical time series are limited in that they are designed for unimodal time series, such as a sequence of structured features (e. g., lab values and vitals signs) or an individual high-dimensional physiological signal (e. g., an electrocardiogram).
no code implementations • 6 Jul 2023 • Emily Mu, John Guttag, Maggie Makar
Given a similarity metric, contrastive methods learn a representation in which examples that are similar are pushed together and examples that are dissimilar are pulled apart.
1 code implementation • 18 Apr 2023 • Rajiv Movva, Divya Shanmugam, Kaihua Hou, Priya Pathak, John Guttag, Nikhil Garg, Emma Pierson
Across outcomes and metrics, we show that the risk scores exhibit significant granular performance disparities within coarse race groups.
1 code implementation • 15 Apr 2023 • Jose Javier Gonzalez Ortiz, John Guttag, Adrian Dalca
In this work, we identify a fundamental and previously unidentified problem that contributes to the challenge of training hypernetworks: a magnitude proportionality between the inputs and outputs of the hypernetwork.
1 code implementation • ICCV 2023 • Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R. Sabuncu, John Guttag, Adrian V. Dalca
We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training.
no code implementations • 11 Apr 2023 • Jose Javier Gonzalez Ortiz, John Guttag, Adrian Dalca
We find that SSHN consistently provides a better accuracy-efficiency trade-off at a fraction of the training cost.
no code implementations • 5 Nov 2022 • Kathleen M. Lewis, John Guttag
Online clothing catalogs lack diversity in body shape and garment size.
no code implementations • 27 Jun 2022 • Helen Lu, Divya Shanmugam, Harini Suresh, John Guttag
Test-time augmentation -- the aggregation of predictions across transformed examples of test inputs -- is an established technique to improve the performance of image classification models.
1 code implementation • 9 Apr 2022 • Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev, John Guttag, Collin M. Stultz
In experiments, considering three datasets and eight predictive tasks, we find that TaskAug is competitive with or improves on prior work, and the learned policies shed light on what transformations are most effective for different tasks.
no code implementations • 30 Mar 2022 • Andrew Hoopes, Malte Hoffmann, Douglas N. Greve, Bruce Fischl, John Guttag, Adrian V. Dalca
We design a meta network, or hypernetwork, that predicts the parameters of a registration network for input hyperparameters, thereby comprising a single model that generates the optimal deformation field corresponding to given hyperparameter values.
3 code implementations • 21 Jun 2021 • Davis Blalock, John Guttag
Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning.
1 code implementation • 4 Mar 2021 • Aniruddh Raghu, John Guttag, Katherine Young, Eugene Pomerantsev, Adrian V. Dalca, Collin M. Stultz
Inference of latent variables in this model corresponds to both making a prediction and providing supporting evidence for that prediction.
1 code implementation • 4 Jan 2021 • Andrew Hoopes, Malte Hoffmann, Bruce Fischl, John Guttag, Adrian V. Dalca
We present HyperMorph, a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training.
no code implementations • 1 Jan 2021 • Maggie Makar, Lauren West, David Hooper, Eric Horvitz, Erica Shenoy, John Guttag
In this work we ask: can we build reliable infection prediction models when the observed data is collected under limited, and biased testing that prioritizes testing symptomatic individuals?
no code implementations • ICCV 2021 • Divya Shanmugam, Davis Blalock, Guha Balakrishnan, John Guttag
In this paper, we present 1) experimental analyses that shed light on cases in which the simple average is suboptimal and 2) a method to address these shortcomings.
no code implementations • 20 Jul 2020 • 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.
no code implementations • MIDL 2019 • Marianne Rakic, John Guttag, Adrian V. Dalca
We present a method that predicts how a brain MRI for an individual will change over time.
1 code implementation • 6 Mar 2020 • Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, John Guttag
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.
no code implementations • 30 Nov 2019 • Ava P. Soleimany, Harini Suresh, Jose Javier Gonzalez Ortiz, Divya Shanmugam, Nil Gural, John Guttag, Sangeeta N. Bhatia
Global eradication of malaria depends on the development of drugs effective against the silent, yet obligate liver stage of the disease.
Cultural Vocal Bursts Intensity Prediction Image Segmentation +2
no code implementations • ICML 2020 • Maggie Makar, Fredrik D. Johansson, John Guttag, David Sontag
Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics.
1 code implementation • NeurIPS 2019 • Adrian V. Dalca, Marianne Rakic, John Guttag, Mert R. Sabuncu
We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks.
1 code implementation • 8 Mar 2019 • Adrian V. Dalca, Guha Balakrishnan, John Guttag, Mert R. Sabuncu
We present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that uses insights from classical registration methods and makes use of recent developments in convolutional neural networks (CNNs).
Ranked #2 on Diffeomorphic Medical Image Registration on OASIS+ADIBE+ADHD200+MCIC+PPMI+HABS+HarvardGSP
Constrained Diffeomorphic Image Registration Deformable Medical Image Registration +2
6 code implementations • 8 Mar 2019 • Adrian V. Dalca, John Guttag, Mert R. Sabuncu
In this work, we introduce a general probabilistic model that describes sparse high dimensional imaging data as being generated by a deep non-linear embedding.
2 code implementations • CVPR 2018 • Adrian V. Dalca, John Guttag, Mert R. Sabuncu
The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable.
no code implementations • 17 Dec 2018 • Kathleen M. Lewis, Natalia S. Rost, John Guttag, Adrian V. Dalca
We present a learning-based registration method, SparseVM, that is more accurate and orders of magnitude faster than the most accurate clinical registration methods.
no code implementations • 2 Dec 2018 • Divya Shanmugam, Davis Blalock, John Guttag
We focus on estimating a patient's risk of cardiovascular death after an acute coronary syndrome based on a patient's raw electrocardiogram (ECG) signal.
7 code implementations • 14 Sep 2018 • Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John Guttag, Adrian V. Dalca
In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images.
Ranked #1 on Diffeomorphic Medical Image Registration on OASIS+ADIBE+ADHD200+MCIC+PPMI+HABS+HarvardGSP (Dice metric)
Deformable Medical Image Registration Diffeomorphic Medical Image Registration +1
1 code implementation • 7 Jun 2018 • Harini Suresh, Jen J. Gong, John Guttag
In this work, we present a two-step framework to 1) learn relevant patient subgroups, and 2) predict an outcome for separate patient populations in a multi-task framework, where each population is a separate task.
2 code implementations • 11 May 2018 • Adrian V. Dalca, Guha Balakrishnan, John Guttag, Mert R. Sabuncu
We demonstrate our method on a 3D brain registration task, and provide an empirical analysis of the algorithm.
1 code implementation • CVPR 2018 • Guha Balakrishnan, Amy Zhao, Adrian V. Dalca, Fredo Durand, John Guttag
Given an image of a person and a desired pose, we produce a depiction of that person in that pose, retaining the appearance of both the person and background.
3 code implementations • CVPR 2018 • Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John Guttag, Adrian V. Dalca
We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest.
no code implementations • ICLR 2018 • Divya Shanmugam, Davis Blalock, John Guttag
Computing distances between examples is at the core of many learning algorithms for time series.
no code implementations • 13 Dec 2016 • Ronnachai Jaroensri, Amy Zhao, Guha Balakrishnan, Derek Lo, Jeremy Schmahmann, John Guttag, Fredo Durand
The performance of our system is comparable to that of a group of ataxia specialists in terms of mean error and correlation, and our system's predictions were consistently within the range of inter-rater variability.
no code implementations • CVPR 2013 • Guha Balakrishnan, Fredo Durand, John Guttag
We extract heart rate and beat lengths from videos by measuring subtle head motion caused by the Newtonian reaction to the influx of blood at each beat.