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Found 20 papers, 6 papers with code
FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
We investigate theory and algorithms for pool-based active learning for multiclass classification using multinomial logistic regression.
A Scalable Algorithm for Active Learning
FIRAL is a recently proposed deterministic active learning algorithm for multiclass classification using logistic regression.
A single-snapshot inverse solver for two-species graph model of tau pathology spreading in human Alzheimer disease
Our method demonstrates an average improvement of 25. 7% relative error compared to the FK model on the AD dataset.
CLAIRE -- Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications
CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in a reasonable time.
Quantitative in vivo imaging to enable tumor forecasting and treatment optimization
Current clinical decision-making in oncology relies on averages of large patient populations to both assess tumor status and treatment outcomes.
RCHOL: Randomized Cholesky Factorization for Solving SDD Linear Systems
We introduce a randomized algorithm, namely RCHOL, to construct an approximate Cholesky factorization for a given Laplacian matrix (a. k. a., graph Laplacian).
Numerical Analysis Mathematical Software Numerical Analysis
ANODEV2: A Coupled Neural ODE Framework
It has been observed that residual networks can be viewed as the explicit Euler discretization of an Ordinary Differential Equation (ODE).
ANODEV2: A Coupled Neural ODE Evolution Framework
It has been observed that residual networks can be viewed as the explicit Euler discretization of an Ordinary Differential Equation (ODE).
ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs
ANODE has a memory footprint of O(L) + O(N_t), with the same computational cost as reversing ODE solve.
Clustering Multivariate Time Series
Multivariate Time Series Imputation
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
A Novel Domain Adaptation Framework for Medical Image Segmentation
Our biophysics based domain adaptation achieves better results, as compared to the existing state-of-the-art GAN model used to create synthetic data for training.
CLAIRE: A distributed-memory solver for constrained large deformation diffeomorphic image registration
We present the formulation, discuss algorithmic features, describe the software package, and introduce an improved preconditioner for the reduced space Hessian to speed up the convergence of our solver.
PDE-constrained optimization in medical image analysis
We review related literature and give examples on the formulation, discretization, and numerical solution of PDE-constrained optimization problems for medical imaging.
Distributed-memory large deformation diffeomorphic 3D image registration
We are able to solve the registration problem for images of this size in less than five seconds on 64 x86 nodes of TACC's "Maverick" system.
A Semi-Lagrangian two-level preconditioned Newton-Krylov solver for constrained diffeomorphic image registration
We compare the performance of our solver against our initial implementation that uses the same spatial discretization but a standard, explicit, second-order Runge-Kutta scheme for the numerical time integration of the transport equations and a single-level preconditioner.
Computational Efficiency
Constrained Diffeomorphic Image Registration
+2
AccFFT: A library for distributed-memory FFT on CPU and GPU architectures
We present a new library for parallel distributed Fast Fourier Transforms (FFT).
Distributed, Parallel, and Cluster Computing
Constrained $H^1$-regularization schemes for diffeomorphic image registration
This allows us to explicitly control the compressibility of the deformation map and by that the determinant of the deformation gradient.
ASKIT: Approximate Skeletonization Kernel-Independent Treecode in High Dimensions
We present a fast algorithm for kernel summation problems in high-dimensions.
Far-Field Compression for Fast Kernel Summation Methods in High Dimensions
We consider fast kernel summations in high dimensions: given a large set of points in $d$ dimensions (with $d \gg 3$) and a pair-potential function (the {\em kernel} function), we compute a weighted sum of all pairwise kernel interactions for each point in the set.
An inexact Newton-Krylov algorithm for constrained diffeomorphic image registration
We augment this regularization scheme with a constraint on the divergence of the velocity field rendering the deformation incompressible and thus ensuring that the determinant of the deformation gradient is equal to one, up to the numerical error.
Computational Efficiency
Constrained Diffeomorphic Image Registration
+1
