Breast cancer exhibits significant spatiotemporal heterogeneity. Traditional radiomics approaches usually rely on low-temporal-resolution imaging and discrete image phases, failing to capture the rapid and continuous kinetic evolution within the tumor microenvironment. Although ultrafast dynamic contrast-enhanced magnetic resonance imaging captures precise contrast agent permeation, current methodologies lack systematic, dedicated computational frameworks to extract these high-dimensional dynamic features. To bridge this gap, this study developed and validated a robust two-stage spatiotemporal radiomics framework for high-dimensional feature selection and predictive modeling. First, a novel gradient-dynamics-based feature selection algorithm was designed to robustly identify highly discriminative kinetic trajectory patterns and overcome the dimensionality curse of time-series data. Second, a lightweight Transformer-based predictive network incorporating a self-attention knowledge distillation mechanism was deployed to capture deep representational knowledge and enhance the differentiation between benign and malignant lesions. Experimental results demonstrated that the proposed framework outperformed traditional baseline models using delta features or pharmacokinetic parameters, achieving an area under the curve of 0.959 ± 0.022 and an accuracy of 92
Novel view synthesis, which is essential for three-dimensional (3D) reconstruction, traditionally requires dense image sets and pre-calibrated camera parameters. However, in industrial digital twin applications spatial constraints often limit data acquisition to sparse views that fail to provide the feature correspondences necessary for accurate modeling. To overcome this bottleneck, a semantic-guided 3D Gaussian splatting (3DGS) framework tailored to sparse-view industrial reconstruction was introduced. Rather than relying on precise initialization and dense inputs, the proposed method couples explicit 3D Gaussian representations with the dense and unconstrained stereo 3D reconstruction (DUSt3R) end-to-end pose estimation model. The severe occlusions, typical of factory environments, are handled by incorporating Segment Anything Model 2 (SAM2) to hierarchically decompose the scene, yielding a structured representation that improves optimization stability. A probability density field-driven algorithm is subsequently applied to extract lightweight meshes directly from the optimized Gaussian point clouds. Evaluations on the MipNeRF360 benchmark and a custom industrial dataset demonstrated substantial improvements in the structural similarity index measure. By enabling robust reconstruction from limited viewpoints, this pipeline offers a practical geometric foundation for automated inspection and remote equipment monitoring.
Abstract Large language models (LLMs) have been rapidly adopted in healthcare since 2022; however, field-level trends and specialty differences remain poorly characterized. This study aims to map the research landscape of LLMs in healthcare and generate comparative specialty profiles through a bibliometric analysis using the Web of Science Core Collection (2015–2025), including English-language articles and reviews analyzed for publication and citation trends, leading countries, institutions, authors, journals, co-citation networks, and keyword structures with VOSviewer and CiteSpace, with sub-analyses for medicine, general and internal; surgery; and radiology, nuclear medicine and medical imaging. Of the 2226 articles included, the annual output increased from four publications in 2022 to 1327 in 2025, yielding a compound annual growth rate (CAGR) of 592.26%; the United States of America contributed 43.5% of the publications, followed by China (14.3%), Germany (9.2%), Turkey (9.0%), and England (6.8%); the publications for each specialty were surgery (31.7%), medicine, general and internal (24.2%), and radiology, nuclear medicine and medical Iimaging (13.0%), with the last demonstrating the fastest 2023–2024 growth (CAGR 265%); and the collaboration networks were United States of America-centered, with dense trans-Atlantic ties. LLM research in healthcare is expanding rapidly with distinct specialty-specific trajectories, and interpreting these trajectories using the DECIDE-AI (developmental and exploratory clinical Iinvestigations of decision support systems driven by artificial intelligence) framework clarifies low-risk near-term applications and monitoring priorities, providing a specialty-aware baseline to support the safe, equitable, and regulation-aligned adoption of LLMs in clinical practice.
Abstract This study focuses on tertiary lymphoid structure (TLS) semantic segmentation in whole slide images (WSIs). Unlike TLS binary segmentation, TLS semantic segmentation identifies boundaries and maturity and requires the integration of contextual information to discover discriminative features. Owing to the extensive scale of WSI (e.g., 100,000 $$\times$$ 100,000 pixels), TLS segmentation is typically performed using a patch-based strategy. However, this prevents the model from accessing information outside the patches, thereby limiting its performance. To address this issue, GCUNet, a graph neural network-based contextual learning network for TLS semantic segmentation, is proposed. Given an image patch (target) to be segmented, GCUNet first progressively aggregates the long-range and fine-grained contexts outside the target. Subsequently, a detail and context fusion block (DCFusion) was designed to integrate the context and details of the target to predict the segmentation mask. This study builds four TLS semantic segmentation datasets: TCGA-COAD, TCGA-LUSC, TCGA-BLCA, and PUMCH-PAAD. The first three, comprising 826 WSIs and 15,276 TLSs, will be made publicly available to promote TLS semantic segmentation. Experiments on these datasets demonstrate that GCUNet consistently improves the mean F1-score (mF1) performance compared with existing state-of-the-art methods, with an observed mF1 improvement of at least 7.41% over the best-performing baseline. These results highlight the potential of GCUNet for accurate TLS assessment and facilitate the development of computational pathology-based immune microenvironment analysis.
Text-to-motion generation aims to synthesize semantically consistent and naturally coherent motion sequences from natural language descriptions. Given the continuous nature of human motion, diffusion models operating in a continuous latent space offer inherent advantages over vector quantization-based methods, particularly in avoiding quantization errors and in modeling quality. However, existing diffusion models primarily rely on mean squared error loss. This stepwise regression paradigm often leads to ‘over-smoothed’ motion sequences and struggles to capture the subtle semantic nuances embedded in textual descriptions. To realize the potential for continuous diffusion generation, an enhanced latent-space diffusion framework designed to elevate generation capabilities across two dimensions, namely, distribution approximation and semantic alignment, is proposed. Specifically, a latent-space adversarial discriminator is incorporated. By applying decoupled adversarial supervision, this component mitigates the detail loss caused by mean regression, significantly enhancing the physical realism and dynamic sharpness. Concurrently, a latent-space contrastive alignment strategy is introduced during the denoising process that reinforces the correspondence of the generated motion sequences with the given textual inputs via explicit cross-modal constraints. Extensive experiments on standard benchmarks demonstrate that the proposed method effectively addresses the limitations of conventional diffusion models, thus validating the potential of continuous diffusion frameworks within the domain of text-driven motion synthesis.
