Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
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 +6 more
wiley +1 more source
Spatiotemporal heterogeneity and socioeconomic determinants of syphilis incidence in Xining, Northwest China: a geospatial analysis. [PDF]
Ren Y, Shi Y, Zhao Y, Fu L, Lin J, Li W.
europepmc +1 more source
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley +1 more source
Based on "space-culture-role" three-dimensional integration perspective for construction of characteristic conservation areas for traditional villages in Southwest Zhejiang, China. [PDF]
Zhao X, Tao J, Liu F.
europepmc +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
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 +5 more
wiley +1 more source
Divergent genetic and phenotypic trajectories in China' s maize hybrids. [PDF]
Li G, Mao H, Li S, Wang H.
europepmc +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
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
Open RGB imaging workflow for morphological and morphometric analysis of fruits using deep learning: a case study on almonds. [PDF]
Mas-Gómez J +3 more
europepmc +1 more source
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu +11 more
wiley +1 more source
Empowering Expert Judgment: A Data-Driven Decision Framework for Standard Setting in High-Dimensional and Data-Scarce Assessments. [PDF]
Zheng T, Jiang Z, Guo Z, Liu Y.
europepmc +1 more source

