Results 181 to 190 of about 82,326 (277)

GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit. [PDF]

Plant Phenomics, 2021
Hartley ZKJ, Jackson AS, Pound M, French AP.   +3 more
europepmc   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Towards bridging the synthetic-to-real gap in quantitative photoacoustic tomography via unsupervised domain adaptation. [PDF]

Photoacoustics
Wang Z, Tao W, Zhang Z, Zhao H.
europepmc   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Unsupervised Domain Adaptation Method Based on Relative Entropy Regularization and Measure Propagation. [PDF]

Entropy (Basel)
Tan L, Peng Z, Song Y, Liu X, Jiang H, Liu S, Wu W, Xiang Z.   +7 more
europepmc   +1 more source

Unsupervised Domain Adaptation for Vertebrae Detection and Identification in 3D CT Volumes Using a Domain Sanity Loss. [PDF]

J Imaging, 2022
Sager P, Salzmann S, Burn F, Stadelmann T.   +3 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Histogram matching-enhanced adversarial learning for unsupervised domain adaptation in medical image segmentation. [PDF]

Med Phys
Qian X, Shao HC, Li Y, Lu W, Zhang Y.
europepmc   +1 more source

Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation. [PDF]

Med Image Comput Comput Assist Interv, 2021
Shin SY, Lee S, Summers RM.
europepmc   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source