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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
TLDR
This study assesses the state-of-the-art machine learning 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, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs
TLDR
ANODE is proposed, an Adjoint based Neural ODE framework which avoids the numerical instability related problems, and provides unconditionally accurate gradients, and discusses a memory efficient algorithm which can further reduce this footprint with a trade-off of additional computational cost.
An image-driven parameter estimation problem for a reaction–diffusion glioma growth model with mass effects
TLDR
The two main goals are to improve the deformable registration from images of brain tumor patients to a common stereotactic space, thereby assisting in the construction of statistical anatomical atlases and to develop predictive capabilities for glioma growth, after the model parameters are estimated for a given patient.
PVFMM: A Parallel Kernel Independent FMM for Particle and Volume Potentials
TLDR
This paper describes the implementation of a parallel fast multipole method for evaluating potentials for discrete and continuous source distributions and discusses several algorithmic improvements and performance optimizations including cache locality, vectorization, shared memory parallelism and use of coprocessors.
Bottom-Up Construction and 2: 1 Balance Refinement of Linear Octrees in Parallel
TLDR
New parallel algorithms for the construction and 2:1 balance refinement of large linear octrees on distributed memory machines, used in many problems in computational science and engineering, are proposed.
High Resolution Forward And Inverse Earthquake Modeling on Terascale Computers
TLDR
Results for material and source inversion of high-resolution models of basins undergoing antiplane motion using parallel scalable inversion algorithms that overcome many of the difficulties particular to inverse heterogeneous wave propagation problems are presented.
Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.
TLDR
The results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers.
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