Unlike prior methods, the research team leveraged unsupervised segmentation—typically used for identifying structures within a larger image—to support hierarchical classification. They ...

Abstract: Deep learning (DL) is currently the dominant approach to image classification and segmentation, but the performances of DL methods are remarkably influenced by the quantity and quality of ...

Hand-Gesture-2-Robot is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to recognize hand ...

Abstract: Deep learning-based semantic segmentation has been the dominant solution to quickly capture regions of interest (ROIs) in remote sensing (RS) images. However, the annotation and training ...

Kaizen rethinks cell segmentation by mimicking brain predictions. Using an iterative machine-learning approach to refine boundaries in crowded microscopy images, it enhances accuracy in tissue studies ...

This project focuses on analyzing customer behavior to uncover patterns that drive retention and churn. Using data-driven insights, we segment customers into distinct groups and build predictive ...

Deep learning has been widely applied to high-dimensional hyperspectral image classification and has achieved significant improvements in classification accuracy. However, most current hyperspectral ...

In this paper, we innovatively propose a multi-view (coronal and transverse) attention network for semi-supervised 3D cardiac image segmentation. In this way, the proposed method obtained more ...

Master data science in 2025. Complete guide to machine learning, big data analytics, Python programming, statistical modeling ...

Master artificial intelligence in 2025 with this comprehensive guide. Explore AI fundamentals, machine learning, deep ...