38 citations · 64 across the 5 of their papers we have counts for
5 papers
Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation
McKell Woodland, Nihil Patel, Austin Castelo +13
Clinically deployed deep learning-based segmentation models are known to fail on data outside of their training distributions. While clinicians review the segmentations, these mode…
Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend
McKell Woodland, Austin Castelo, Mais Al Taie +8
Fréchet Inception Distance (FID) is a widely used metric for assessing synthetic image quality. It relies on an ImageNet-based feature extractor, making its applicability to medica…
Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation
McKell Woodland, Nihil Patel, Mais Al Taie +4
Clinically deployed segmentation models are known to fail on data outside of their training distribution. As these models perform well on most cases, it is imperative to detect out…
StyleGAN2-based Out-of-Distribution Detection for Medical Imaging
McKell Woodland, John Wood, Caleb O'Connor +2
One barrier to the clinical deployment of deep learning-based models is the presence of images at runtime that lie far outside the training distribution of a given model. We aim to…
Evaluating the Performance of StyleGAN2-ADA on Medical Images
McKell Woodland, John Wood, Brian M. Anderson +12
Although generative adversarial networks (GANs) have shown promise in medical imaging, they have four main limitations that impeded their utility: computational cost, data requirem…