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Design of a 3D emotion mapping model for visual feature analysis using improved Gaussian mixture models.
PeerJ Comput Sci
December 2024
Information and Computer Science Department, King Fahad University of Petroleum and Minerals, Dhahran, Saudi Arabia.
Given the integration of color emotion space information from multiple feature sources in multimodal recognition systems, effectively fusing this information presents a significant challenge. This article proposes a three-dimensional (3D) color-emotion space visual feature extraction model for multimodal data integration based on an improved Gaussian mixture model to address these issues. Unlike traditional methods, which often struggle with redundant information and high model complexity, our approach optimizes feature fusion by employing entropy and visual feature sequences.
View Article and Find Full Text PDFSpatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity.
Brief Bioinform
November 2024
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Spatially resolved transcriptomics (SRT) technologies facilitate the exploration of cell fates or states within tissue microenvironments. Despite these advances, the field has not adequately addressed the regulatory heterogeneity influenced by microenvironmental factors. Here, we propose a novel Spatially Aligned Graph Transfer Learning (SpaGTL), pretrained on a large-scale multi-modal SRT data of about 100 million cells/spots to enable inference of context-specific spatial gene regulatory networks across multiple scales in data-limited settings.
View Article and Find Full Text PDFObjectives: Combining Computed Tomography (CT) intuitive anatomical features with Three-Dimensional (3D) CT multimodal radiomic imaging features to construct a model for assessing the aggressiveness of pancreatic neuroendocrine tumors (pNETs) prior to surgery.
Methods: This study involved 242 patients, randomly assigned to training (170) and validation (72) cohorts. Preoperative CT and 3D CT radiomic features were used to develop a model predicting pNETs aggressiveness.
Multiorifice acoustic microrobot for boundary-free multimodal 3D swimming.
Proc Natl Acad Sci U S A
January 2025
Physical Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart 70569, Germany.
The emerging new generation of small-scaled acoustic microrobots is poised to expedite the adoption of microrobotics in biomedical research. Recent designs of these microrobots have enabled intricate bioinspired motions, paving the way for their real-world applications. We present a multiorifice design of air-filled spherical microrobots that convert acoustic wave energy to efficient propulsion through a resonant encapsulated microbubble.
View Article and Find Full Text PDFA multi-modal dental dataset for semi-supervised deep learning image segmentation.
Sci Data
January 2025
School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China.
In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Additionally, deep learning for tooth segmentation can focus on relevant treatment information and localize lesions.
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