259 citations · 353 across the 3 of their papers we have counts for
6 papers
PEP: Parameter Ensembling by Perturbation
Alireza Mehrtash, Purang Abolmaesumi, Polina Golland +3
Ensembling is now recognized as an effective approach for increasing the predictive performance and calibration of deep networks. We introduce a new approach, Parameter Ensembling…
Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation
Alireza Mehrtash, William M. Wells, Clare M. Tempany +2
Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Mor…
Deep Information Theoretic Registration
Alireza Sedghi, Jie Luo, Alireza Mehrtash +5
This paper establishes an information theoretic framework for deep metric based image registration techniques. We show an exact equivalence between maximum profile likelihood and m…
Semi-Supervised Deep Metrics for Image Registration
Alireza Sedghi, Jie Luo, Alireza Mehrtash +5
Deep metrics have been shown effective as similarity measures in multi-modal image registration; however, the metrics are currently constructed from aligned image pairs in the trai…
GBM Volumetry using the 3D Slicer Medical Image Computing Platform
Jan Egger, Tina Kapur, Andriy Fedorov +7
Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MR…
Pituitary Adenoma Volumetry with 3D Slicer
Jan Egger, Tina Kapur, Christopher Nimsky +1
In this study, we present pituitary adenoma volumetry using the free and open source medical image computing platform for biomedical research: (3D) Slicer. Volumetric changes in ce…