Search Results for author: Matthew Toews

Found 7 papers, 2 papers with code

GPU optimization of the 3D Scale-invariant Feature Transform Algorithm and a Novel BRIEF-inspired 3D Fast Descriptor

1 code implementation19 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.

Keypoint Detection

Curating Subject ID Labels using Keypoint Signatures

no code implementations7 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.

Efficient Pairwise Neuroimage Analysis using the Soft Jaccard Index and 3D Keypoint Sets

1 code implementation11 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.

A Keypoint-based Morphological Signature for Large-scale Neuroimage Analysis

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.

Image Retrieval

Keypoint Transfer for Fast Whole-Body Segmentation

no code implementations22 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.

Semantic Segmentation

On the Applicability of Registration Uncertainty

no code implementations14 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.

Image Registration

Modeling Information Flow Through Deep Neural Networks

no code implementations29 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).

Classification General Classification +1

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