Results 41 to 50 of about 8,876 (216)
, 2022 Automatic surface defect detection is critical for manufacturing industries, such as steel, fabric, and marble industries. This study proposes a Swin transformer-based model called Multi-Feature Integration Network (Swin-MFINet) for pixel-level surface ...
Türkoğlu, Muammer +8 more
core +1 more source
A wheat spike detection method based on Transformer
Frontiers in Plant Science, 2022 Wheat spike detection has important research significance for production estimation and crop field management. With the development of deep learning-based algorithms, researchers tend to solve the detection task by convolutional neural networks (CNNs ...
Qiong Zhou +11 more
doaj +1 more source
A Swin Transformer-Based Encoding Booster Integrated in U-Shaped Network for Building Extraction
Remote Sensing, 2022 Building extraction is a popular topic in remote sensing image processing. Efficient building extraction algorithms can identify and segment building areas to provide informative data for downstream tasks.
Xiao Xiao +5 more
doaj +1 more source
Evaluation and Mitigation of Faults Affecting Swin Transformers
2023 IEEE 29th International Symposium on On-Line Testing and Robust System Design (IOLTS), 2023 In the last decade, a huge effort has been spent on assessing the reliability of Convolutional Neural networks (CNNs), probably the most popular architecture for image classification tasks. However, modern Deep Neural Networks (DNNs) are rapidly overtaking CNNs, as state-of-the-art results for many tasks are achieved with the Transformers, innovative ...
Gabriele Gavarini +2 more
openaire +2 more sources
Frontiers in OncologyCervical cancer is a prevalent and concerning disease affecting women, with increasing incidence and mortality rates. Early detection plays a crucial role in improving outcomes.
Manal Abdullah Alohali +7 more
doaj +1 more source
AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling
Advanced Science, EarlyView.This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi +4 more
wiley +1 more source
Deep Reinforcement Learning with Swin Transformers
Proceedings of the 2024 8th International Conference on Digital Signal ProcessingTransformers are neural network models that utilize multiple layers of self-attention heads and have exhibited enormous potential in natural language processing tasks. Meanwhile, there have been efforts to adapt transformers to visual tasks of machine learning, including Vision Transformers and Swin Transformers.
Li Meng +3 more
openaire +2 more sources
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
Advanced Intelligent Discovery, EarlyView.The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
When Biology Meets Medicine: A Perspective on Foundation Models
Advanced Intelligent Discovery, EarlyView.Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
, 2022 13 pages, 3 ...
Ali Hatamizadeh +5 more
openaire +2 more sources