109 citations · 156 across the 12 of their papers we have counts for
12 papers
Feature Gradient Flow for Interpreting Deep Neural Networks in Head and Neck Cancer Prediction
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. The gradient flow of a mo…
NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Models
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 sett…
Modeling the Shape of the Brain Connectome via Deep Neural Networks
Haocheng Dai, Martin Bauer, P. Thomas Fletcher +1
The goal of diffusion-weighted magnetic resonance imaging (DWI) is to infer the structural connectivity of an individual subject's brain in vivo. To statistically study the variabi…
Integrated Construction of Multimodal Atlases with Structural Connectomes in the Space of Riemannian Metrics
Kristen M. Campbell, Haocheng Dai, Zhe Su +3
The structural network of the brain, or structural connectome, can be represented by fiber bundles generated by a variety of tractography methods. While such methods give qualitati…
Structural Connectome Atlas Construction in the Space of Riemannian Metrics
Kristen M. Campbell, Haocheng Dai, Zhe Su +3
The structural connectome is often represented by fiber bundles generated from various types of tractography. We propose a method of analyzing connectomes by representing them as a…
The Adversarial Attack and Detection under the Fisher Information Metric
Chenxiao Zhao, P. Thomas Fletcher, Mixue Yu +3
Many deep learning models are vulnerable to the adversarial attack, i.e., imperceptible but intentionally-designed perturbations to the input can cause incorrect output of the netw…
The Riemannian Geometry of Deep Generative Models
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. Under certain regularity conditions, these models parameterize nonlinea…
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference
Abhishek Kumar, Prasanna Sattigeri, P. Thomas Fletcher
Semi-supervised learning methods using Generative Adversarial Networks (GANs) have shown promising empirical success recently. Most of these methods use a shared discriminator/clas…
Local Group Invariant Representations via Orbit Embeddings
Anant Raj, Abhishek Kumar, Youssef Mroueh +2
Invariance to nuisance transformations is one of the desirable properties of effective representations. We consider transformations that form a \emph{group} and propose an approach…
Hierarchical Graphical Models for Multigroup Shape Analysis using Expectation Maximization with Sampling in Kendall's Shape Space
Yen-Yun Yu, P. Thomas Fletcher, Suyash P. Awate
This paper proposes a novel framework for multi-group shape analysis relying on a hierarchical graphical statistical model on shapes within a population.The framework represents in…
Polynomial Regression on Riemannian Manifolds
Jacob Hinkle, Prasanna Muralidharan, P. Thomas Fletcher +1
In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape ana…
Computing Hulls And Centerpoints In Positive Definite Space
P. Thomas Fletcher, John Moeller, Jeff M. Phillips +1
In this paper, we present algorithms for computing approximate hulls and centerpoints for collections of matrices in positive definite space. There are many applications where the…