5 citations · 10 across the 12 of their papers we have counts for
14 papers
FleXray: Universal Clinical X-ray Segmentation
Victor Ion Butoi, Vivek Gopalakrishnan, John V. Guttag +2
X-ray is medicine's most widely used imaging modality, yet remains among its least quantitative. Unlike volumetric modalities like CT or MRI, X-ray collapses 3D anatomy into a 2D p…
A Removal Based Approach to Improve LLM Faithfulness at Test-Time
Qinglan Luo, S M A Nahian, John Guttag +2
Large language models (LLMs) are increasingly used for consequential decisions, making their explanations an important tool for auditing model behavior. Unfortunately, these explan…
Improving Domain Generalization in Contrastive Learning using Adaptive Temperature Control
Robert Lewis, Katie Matton, Rosalind W. Picard +1
Self-supervised pre-training with contrastive learning is a powerful method for learning from sparsely labeled data. However, performance can drop considerably when there is a shif…
Unified Brain Surface and Volume Registration
S. Mazdak Abulnaga, Andrew Hoopes, Malte Hoffmann +6
Accurate registration of brain MRI scans is fundamental for cross-subject analysis in neuroscientific studies. This involves aligning both the cortical surface of the brain and the…
Test-time augmentation improves efficiency in conformal prediction
Divya Shanmugam, Helen Lu, Swami Sankaranarayanan +1
A conformal classifier produces a set of predicted classes and provides a probabilistic guarantee that the set includes the true class. Unfortunately, it is often the case that con…
Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations
Katie Matton, Robert Osazuwa Ness, John Guttag +1
Large language models (LLMs) are capable of generating plausible explanations of how they arrived at an answer to a question. However, these explanations can misrepresent the model…
MultiMorph: On-demand Atlas Construction
S. Mazdak Abulnaga, Andrew Hoopes, Neel Dey +5
We present MultiMorph, a fast and efficient method for constructing anatomical atlases on the fly. Atlases capture the canonical structure of a collection of images and are essenti…
Evaluating multiple models using labeled and unlabeled data
Divya Shanmugam, Shuvom Sadhuka, Manish Raghavan +3
It remains difficult to evaluate machine learning classifiers in the absence of a large, labeled dataset. While labeled data can be prohibitively expensive or impossible to obtain,…
MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance
Hallee E. Wong, Jose Javier Gonzalez Ortiz, John Guttag +1
Medical researchers and clinicians often need to perform novel segmentation tasks on a set of related images. Existing methods for segmenting a new dataset are either interactive,…
Tyche: Stochastic In-Context Learning for Medical Image Segmentation
Marianne Rakic, Hallee E. Wong, Jose Javier Gonzalez Ortiz +3
Existing learning-based solutions to medical image segmentation have two important shortcomings. First, for most new segmentation task, a new model has to be trained or fine-tuned.…
ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image
Hallee E. Wong, Marianne Rakic, John Guttag +1
Biomedical image segmentation is a crucial part of both scientific research and clinical care. With enough labelled data, deep learning models can be trained to accurately automate…
GIST: Generating Image-Specific Text for Fine-grained Object Classification
Kathleen M. Lewis, Emily Mu, Adrian V. Dalca +1
Recent vision-language models outperform vision-only models on many image classification tasks. However, because of the absence of paired text/image descriptions, it remains diffic…
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series
Aniruddh Raghu, Payal Chandak, Ridwan Alam +2
Self-supervised learning (SSL) for clinical time series data has received significant attention in recent literature, since these data are highly rich and provide important informa…
Multi-Similarity Contrastive Learning
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. Cont…