Results 191 to 200 of about 276,670 (296)

Modelling Stochastic Sensor Noise via Mask-Conditioned Diffusion for Data Augmentation in Low-SNR LGE-CMR. [PDF]

Sensors (Basel)
Fernandes S, Barros C, Pinto A, Pereira VH, Lima C, Silva CA.   +5 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

A generative adversarial network with multi-scale structural features for sparse-view photoacoustic tomography reconstruction. [PDF]

Photoacoustics
Li J, Luo X, Ma Y, Li G, Li T, Yuan Z, Shen Y, Ma C, Sun M.   +8 more
europepmc   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Generative AI-Driven CNN Framework for Enhanced Lung Cancer Detection, Prediction, and Treatment: A Novel Approach to Overcoming AI Limitations. [PDF]

Int J Biomed Imaging
Bodicherla SS, Natarajasivan D, Reddy MP.   +2 more
europepmc   +1 more source

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

Advanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

Novel Monolithic CAVET-HEMT Integration for Inverting-Switch Operation. [PDF]

ACS Omega
Tan WH, Zhao H, Farizuan M, Firdaus A, Kawarada H, Tao T, Falina S, Syamsul M.   +7 more
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

An MEM-DMD-Enabled Ghost Imaging System Enhanced by a Hybrid CNN-GAN for High-Resolution Imaging Under Scattering Media. [PDF]

Micromachines (Basel)
Akhter Z, Iqbal R, Janusas G, Urbaite S, Palevicius A.   +4 more
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source