1 code implementation • 19 Dec 2021 • Jean-Baptiste Carluer, Laurent Chauvin, Jie Luo, William M. Wells III, Ines Machado, Rola Harmouche, Matthew Toews
This work details a highly efficient implementation of the 3D scale-invariant feature transform (SIFT) algorithm, for the purpose of machine learning from large sets of volumetric medical image data.
no code implementations • 7 Oct 2021 • Laurent Chauvin, Matthew Toews
Subject ID labels are unique, anonymized codes that can be used to group all images of a subject while maintaining anonymity.
1 code implementation • 11 Mar 2021 • Laurent Chauvin, Kuldeep Kumar, Christian Desrosiers, William Wells III, Matthew Toews
Our measure generalizes the Jaccard index to account for soft set equivalence (SSE) between keypoint elements, via an adaptive kernel framework modeling uncertainty in keypoint appearance and geometry.
no code implementations • MIDL 2019 • Laurent Chauvin, Matthew Toews
We present an image keypoint-based morphological signature that can be used to efficiently assess the pair-wise whole-brain similarity for large MRI datasets.
no code implementations • 22 Jun 2018 • Christian Wachinger, Matthew Toews, Georg Langs, William Wells, Polina Golland
We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images.
no code implementations • 14 Mar 2018 • Jie Luo, Alireza Sedghi, Karteek Popuri, Dana Cobzas, Miaomiao Zhang, Frank Preiswerk, Matthew Toews, Alexandra Golby, Masashi Sugiyama, William M. Wells III, Sarah Frisken
For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters.
no code implementations • 29 Nov 2017 • Ahmad Chaddad, Behnaz Naisiri, Marco Pedersoli, Eric Granger, Christian Desrosiers, Matthew Toews
This paper proposes a principled information theoretic analysis of classification for deep neural network structures, e. g. convolutional neural networks (CNN).