1 citations · 1 across the 7 of their papers we have counts for
8 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…
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…
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…
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…
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…
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…
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels
Sina Alemohammad, Hossein Babaei, Randall Balestriero +8
High dimensionality poses many challenges to the use of data, from visualization and interpretation, to prediction and storage for historical preservation. Techniques abound to red…