2.2k citations · 4.4k across the 41 of their papers we have counts for
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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…
Estimation of Bounds on Potential Outcomes For Decision Making
Maggie Makar, Fredrik D. Johansson, John Guttag +1
Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often…
Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions
Guha Balakrishnan, Adrian V. Dalca, Amy Zhao +3
We introduce visual deprojection: the task of recovering an image or video that has been collapsed along a dimension. Projections arise in various contexts, such as long-exposure p…
Learning Conditional Deformable Templates with Convolutional Networks
Adrian V. Dalca, Marianne Rakic, John Guttag +1
We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks. Conventional methods for te…
Unsupervised Data Imputation via Variational Inference of Deep Subspaces
Adrian V. Dalca, John Guttag, Mert R. Sabuncu
A wide range of systems exhibit high dimensional incomplete data. Accurate estimation of the missing data is often desired, and is crucial for many downstream analyses. Many state-…
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
Adrian V. Dalca, John Guttag, Mert R. Sabuncu
We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data th…
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Adrian V. Dalca, Guha Balakrishnan, John Guttag +1
Classical deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimizati…
Data augmentation using learned transformations for one-shot medical image segmentation
Amy Zhao, Guha Balakrishnan, Frédo Durand +2
Image segmentation is an important task in many medical applications. Methods based on convolutional neural networks attain state-of-the-art accuracy; however, they typically rely…
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
Harini Suresh, John V. Guttag
As machine learning (ML) increasingly affects people and society, awareness of its potential unwanted consequences has also grown. To anticipate, prevent, and mitigate undesirable…