Results 141 to 150 of about 2,963,905 (354)

A Synergistic Strategy for Data‐Constrained Deep Learning in Materials Science

open access: yesMaterials Genome Engineering Advances, EarlyView.
This work develops a three‐stage machine learning framework for materials property prediction, integrating data preparation, graph‐based model training, and final property inference. By synergistically integrating attention pooling, multi‐task learning, auxiliary tasks, and classification‐corrected regression, this hybrid framework provide a ...
Chun Ting Shao   +6 more
wiley   +1 more source

DALC: Distributed Arithmetic Coding Aided by Linear Codes [PDF]

open access: green
Junwei Zhou   +3 more
openalex   +1 more source

Self‐Supervised Deep Learning Framework for Rician Distribution Based Denoising and Modeling of Multi‐b Prostate Diffusion MRI

open access: yesMagnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Convolutional neural networks (CNNs) are evaluated for improved and accelerated denoising and Rician bias correction in multi‐b DW images with simultaneous signal modeling. Methods Prostate diffusion images from 46 individuals acquired at 20 linearly distributed b‐values (bmax=2000s/mm2)$$ {b}_{\mathrm{max}}=2000\kern0.3em \mathrm{s}/{\
Mustafa Abbas   +4 more
wiley   +1 more source

GABA+‐Edited Magnetic Resonance Spectroscopy Deep Learning Quality Assessment Framework

open access: yesMagnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Motivated by the need to improve GABA+‐edited magnetic resonance spectroscopy (MRS) quality, we developed a three‐module framework to improve transient averaging based on quality. We hypothesized that training a deep learning (DL) model to differentiate spectrum quality could improve transient averaging compared to traditional ...
Hanna Bugler   +2 more
wiley   +1 more source

Four Directions, One Solution: Enabling Rapid Diffusion Tensor MRI for Ultra‐Low Field Using Deep Learning

open access: yesMagnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose This study revisits the tetrahedral encoding strategy originally proposed to accelerate Diffusion Tensor Magnetic Resonance Imaging (DT‐MRI) by reducing the requisite number of diffusion‐weighted measurements to four. We examine its practical limitations and explore how artificial intelligence (AI) can extend its utility. Specifically,
Joshua Mawuli Ametepe   +4 more
wiley   +1 more source

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