no code implementations • 18 Apr 2024 • Yinzhu Jin, Matthew B. Dwyer, P. Thomas Fletcher
Our method is based on the principle that if a model is dependent on a feature, then removal of that feature should significantly harm its performance.
no code implementations • 10 Dec 2023 • Tyler Spears, P. Thomas Fletcher
Recent deep learning methods in super-resolving diffusion MRIs have focused on upsampling to a fixed spatial grid, but this does not satisfy tractography's need for a continuous field.
no code implementations • 2 Dec 2023 • Shen Zhu, Ifrah Zawar, Jaideep Kapur, P. Thomas Fletcher
Hippocampal atrophy in Alzheimer's disease (AD) is asymmetric and spatially inhomogeneous.
no code implementations • 24 Jul 2023 • Yinzhu Jin, Jonathan C. Garneau, P. Thomas Fletcher
This paper introduces feature gradient flow, a new technique for interpreting deep learning models in terms of features that are understandable to humans.
no code implementations • 20 Mar 2023 • Aman Shrivastava, P. Thomas Fletcher
In recent years, computational pathology has seen tremendous progress driven by deep learning methods in segmentation and classification tasks aiding prognostic and diagnostic settings.
1 code implementation • 6 Mar 2022 • Haocheng Dai, Martin Bauer, P. Thomas Fletcher, Sarang Joshi
The goal of diffusion-weighted magnetic resonance imaging (DWI) is to infer the structural connectivity of an individual subject's brain in vivo.
1 code implementation • 20 Sep 2021 • Kristen M. Campbell, Haocheng Dai, Zhe Su, Martin Bauer, P. Thomas Fletcher, Sarang C. Joshi
In order to enable population-level statistical analysis of the structural connectome, we propose representing a connectome as a Riemannian metric, which is a point on an infinite-dimensional manifold.
no code implementations • 9 Mar 2021 • Kristen M. Campbell, Haocheng Dai, Zhe Su, Martin Bauer, P. Thomas Fletcher, Sarang C. Joshi
The structural connectome is often represented by fiber bundles generated from various types of tractography.
no code implementations • 9 Oct 2018 • Chenxiao Zhao, P. Thomas Fletcher, Mixue Yu, Yaxin Peng, Guixu Zhang, Chaomin Shen
By considering the data space as a non-linear space with the Fisher information metric induced from a neural network, we first propose an adversarial attack algorithm termed one-step spectral attack (OSSA).
no code implementations • 21 Nov 2017 • Hang Shao, Abhishek Kumar, P. Thomas Fletcher
Deep generative models learn a mapping from a low dimensional latent space to a high-dimensional data space.
no code implementations • NeurIPS 2017 • Abhishek Kumar, Prasanna Sattigeri, P. Thomas Fletcher
Semi-supervised learning methods using Generative Adversarial Networks (GANs) have shown promising empirical success recently.
no code implementations • 6 Dec 2016 • Anant Raj, Abhishek Kumar, Youssef Mroueh, P. Thomas Fletcher, Bernhard Schölkopf
We consider transformations that form a \emph{group} and propose an approach based on kernel methods to derive local group invariant representations.