Results 181 to 190 of about 3,020,093 (290)

Unsupervised Domain Adaptation with Raman Spectroscopy for Rapid Autoimmune Disease Diagnosis. [PDF]

Sensors (Basel)
Zhang Z, Liu Y, Chen C, Lv X, Chen C.
europepmc   +1 more source

Enhanced 3D Pose Estimation in Multi-Person, Multi-View Scenarios through Unsupervised Domain Adaptation with Dropout Discriminator. [PDF]

Sensors (Basel), 2023
Deng J, Yao H, Shi P.
europepmc   +1 more source

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

Advanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser, Haseeb Muhammad, Junaid Younas, Gillian Lovell, Bianca Migliori, Andreas Dengel, Sheraz Ahmed   +6 more
wiley   +1 more source

ADAM-Net: Anatomy-Guided Attentive Unsupervised Domain Adaptation for Joint MG Segmentation and MGD Grading. [PDF]

J Imaging
Fang J, He X, Jiang Y, Wang MH.
europepmc   +1 more source

Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images. [PDF]

Front Artif Intell, 2023
Thiam P, Lausser L, Kloth C, Blaich D, Liebold A, Beer M, Kestler HA.   +6 more
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Correction: Contrastive learning enhanced pseudo-labeling for unsupervised domain adaptation in person re-identification. [PDF]

PLoS One
Bai X, Zhang Y, Zhang C, Wang Z.
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

Learning Domain-Invariant Representations for Event-Based Motion Segmentation: An Unsupervised Domain Adaptation Approach. [PDF]

J Imaging
Jeryo M, Harati A.
europepmc   +1 more source

Unsupervised Domain Adaptation for Image Classification and Object Detection Using Guided Transfer Learning Approach and JS Divergence. [PDF]

Sensors (Basel), 2023
Goel P, Ganatra A.
europepmc   +1 more source