2.2k citations · 5.8k across the 101 of their papers we have counts for
12 papers · 1 filter
Robust Learning via Conditional Prevalence Adjustment
Minh Nguyen, Alan Q. Wang, Heejong Kim +1
Healthcare data often come from multiple sites in which the correlations between confounding variables can vary widely. If deep learning models exploit these unstable correlations,…
Zero-shot Learning of Individualized Task Contrast Prediction from Resting-state Functional Connectomes
Minh Nguyen, Gia H. Ngo, Mert R. Sabuncu
Given sufficient pairs of resting-state and task-evoked fMRI scans from subjects, it is possible to train ML models to predict subject-specific task-evoked activity using resting-s…
Characterizing the Features of Mitotic Figures Using a Conditional Diffusion Probabilistic Model
Cagla Deniz Bahadir, Benjamin Liechty, David J. Pisapia +1
Mitotic figure detection in histology images is a hard-to-define, yet clinically significant task, where labels are generated with pathologist interpretations and where there is no…
A Framework for Interpretability in Machine Learning for Medical Imaging
Alan Q. Wang, Batuhan K. Karaman, Heejong Kim +4
Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretabilit…
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
Alan Q. Wang, Minh Nguyen, Mert R. Sabuncu
Machine learning models will often fail when deployed in an environment with a data distribution that is different than the training distribution. When multiple environments are av…
Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging
Heejong Kim, Victor Ion Butoi, Adrian V. Dalca +2
Most state-of-the-art techniques for medical image segmentation rely on deep-learning models. These models, however, are often trained on narrowly-defined tasks in a supervised fas…
Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources
Suraj Rajendran, Weishen Pan, Mert R. Sabuncu +3
Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clin…
Modulating human brain responses via optimal natural image selection and synthetic image generation
Zijin Gu, Keith Jamison, Mert R. Sabuncu +1
Understanding how human brains interpret and process information is important. Here, we investigated the selectivity and inter-individual differences in human brain responses to im…
Learning to Compare Longitudinal Images
Heejong Kim, Mert R. Sabuncu
Longitudinal studies, where a series of images from the same set of individuals are acquired at different time-points, represent a popular technique for studying and characterizing…
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…
A Robust and Interpretable Deep Learning Framework for Multi-modal Registration via Keypoints
Alan Q. Wang, Evan M. Yu, Adrian V. Dalca +1
We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for r…
Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processing
Xinzi He, Alan Wang, Mert R. Sabuncu
Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space. In this paper, we propose an end-to-end weak…