7 papers
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…
Pre- and Post-Treatment Glioma Segmentation with the Medical Imaging Segmentation Toolkit
Adrian Celaya, Tucker Netherton, Dawid Schellingerhout +3
Medical image segmentation continues to advance rapidly, yet rigorous comparison between methods remains challenging due to a lack of standardized and customizable tooling. In this…
Virtual Dosimetrists: A Radiotherapy Training "Flight Simulator"
Skylar S. Gay, Tucker Netherton, Barbara Marquez +7
Effective education in radiotherapy plan quality review requires a robust, regularly updated set of examples and the flexibility to demonstrate multiple possible planning approache…
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…
MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework
Adrian Celaya, Evan Lim, Rachel Glenn +7
Medical imaging segmentation is a highly active area of research, with deep learning-based methods achieving state-of-the-art results in several benchmarks. However, the lack of st…