6 citations · 10 across the 14 of their papers we have counts for
15 papers
ConRad: Efficient Conformal Prediction for Radiomics
Matt Y. Cheung, Ashok Veeraraghavan, Guha Balakrishnan
Radiomic features derived from medical images and segmentation masks are used to support decision making in clinical imaging pipelines. In practice, these features are often comput…
Splatshot: 3D Face Avatar Generation from a Single Unconstrained Photo
Hao Liang, Zhixuan Ge, Soumendu Majee +3
Reconstructing a photorealistic 3D face avatar from a single unconstrained photograph is challenging: feed-forward 3D Gaussian Splatting (3DGS) models degrade on out-of-distributio…
Conformal Certification of Reasoning Trace Prefixes
Matt Y. Cheung, Ashok Veeraraghavan, Hanjie Chen +1
Language model reasoning traces are rarely all-or-nothing; they frequently contain valid intermediate steps before a critical error occurs. Existing uncertainty quantification meth…
GeoViSTA: Geospatial Vision-Tabular Transformer for Multimodal Environment Representation
Yuhao Liu, Sadeer Al-Kindi, Ashok Veeraraghavan +1
Large-scale pretraining on Earth observation imagery has yielded powerful representations of the natural and built environment. However, most existing geospatial foundation models…
Efficient Conformal Volumetry for Template-Based Segmentation
Matt Y. Cheung, Ashok Veeraraghavan, Guha Balakrishnan
Template-based segmentation, a widely used paradigm in medical imaging, propagates anatomical labels via deformable registration from a labeled atlas to a target image, and is ofte…
Fit Pixels, Get Labels: Meta-learned Implicit Networks for Image Segmentation
Kushal Vyas, Ashok Veeraraghavan, Guha Balakrishnan
Implicit neural representations (INRs) have achieved remarkable successes in learning expressive yet compact signal representations. However, they are not naturally amenable to pre…
COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metrics
Matt Y. Cheung, Ashok Veeraraghavan, Guha Balakrishnan
In clinical applications, the utility of segmentation models is often based on the accuracy of derived downstream metrics such as organ size, rather than by the pixel-level accurac…
FastAvatar: Instant 3D Gaussian Splatting for Faces from Single Unconstrained Poses
Hao Liang, Zhixuan Ge, Soumendu Majee +4
We present FastAvatar, a fast and robust algorithm for single-image 3D face reconstruction using 3D Gaussian Splatting (3DGS). Given a single input image from an arbitrary pose, Fa…
Post-Hurricane Debris Segmentation Using Fine-Tuned Foundational Vision Models
Kooshan Amini, Yuhao Liu, Jamie Ellen Padgett +2
Timely and accurate detection of hurricane debris is critical for effective disaster response and community resilience. While post-disaster aerial imagery is readily available, rob…
When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT
Matt Y. Cheung, Sophia Zorek, Tucker J. Netherton +4
Diffusion models demonstrate state-of-the-art performance on image generation, and are gaining traction for sparse medical image reconstruction tasks. However, compared to classica…
Bias-Aware Conformal Prediction for Metric-Based Imaging Pipelines
Matt Y. Cheung, Tucker J. Netherton, Laurence E. Court +2
Reliable confidence measures of metrics derived from medical imaging reconstruction pipelines would improve the standard of decision-making in many clinical workflows. Conformal Pr…
Downscaling Extreme Precipitation with Wasserstein Regularized Diffusion
Yuhao Liu, James Doss-Gollin, Qiushi Dai +2
Understanding the risks posed by extreme rainfall events requires analysis of precipitation fields with high resolution (to assess localized hazards) and extensive historical cover…
DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rays
Yiran Sun, Hana Baroudi, Tucker Netherton +4
Computed Tomography (CT) scans are the standard-of-care for the visualization and diagnosis of many clinical ailments, and are needed for the treatment planning of external beam ra…
Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction
Matt Y Cheung, Tucker J Netherton, Laurence E Court +2
Modern deep learning reconstruction algorithms generate impressively realistic scans from sparse inputs, but can often produce significant inaccuracies. This makes it difficult to…
ISLAND: Interpolating Land Surface Temperature using land cover
Yuhao Liu, Pranavesh Panakkal, Sylvia Dee +3
Cloud occlusion is a common problem in the field of remote sensing, particularly for retrieving Land Surface Temperature (LST). Remote sensing thermal instruments onboard operation…