Author: McKell Woodland

Preprint available on arXiv: [2311.13717] Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend (arxiv.org)

Served as a reviewer for DGM4H 2023.

I’m volunteering as the lead instructor for a deep learning bootcamp taught to HBCU and community college professors through The Coding School and AWS.

Thank you to the Rice Engineering Alumni grant for the generous graduate student travel grant to help me attend MICCAI 2023!

Won “Best Spotlight Paper” at the Uncertainty for the Safe Utilization of Machine Learning in Medical Imaging (UNSURE) workshop as part of MICCAI 2023.

“Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation” published by Springer in the proceedings of the Uncertainty for the Safe Utilization of Machine Learning in Medical Imaging” workshop associated with MICCAI 2023.

Woodland, M., Patel, N., Al Taie, M., Yung, J.P., Netherton, T.J., Patel, A.B., & Brock, K.K. (2023). Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation. In: Sudre, C.H., Baumgartner, C.F., Dalca, A., Mehta, R., Qin, C., Wells, W.M. (eds) UNSURE 2023. LNCS, vol 14291. Springer, Cham. doi.org/10.1007/978-3-031-44336-7_15

Paper entitled “Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation” accepted into UNSURE (a MICCAI workshop). Paper was awarded a spotlight presentation at the workshop.

Abstract entitled “Interpretable Out-of-Distribution Detection for Medical Images Using Generative Adversarial Networks and the MIDRC Data Repository”.