Deep Learning Methods for Microstructural Image Analysis: The State-of-the-Art and Future Perspectives
Finding quantitative descriptors representing the microstructural features of a given material
is an ongoing research area in the paradigm of Materials-by-Design. Historically, the …
is an ongoing research area in the paradigm of Materials-by-Design. Historically, the …
A hybrid algorithm with Swin transformer and convolution for cloud detection
Cloud detection is critical in remote sensing image processing, and convolutional neural
networks (CNNs) have significantly advanced this field. However, traditional CNNs primarily …
networks (CNNs) have significantly advanced this field. However, traditional CNNs primarily …
Discriminating Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Review
Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of
land cover that benefit from developments in spectral imaging and space technology. The …
land cover that benefit from developments in spectral imaging and space technology. The …
STU-Net: Swin Transformer U-Net for high-throughput live cell analysis with a lens-free on-chip digital holographic microscope
W Lin, Y Chen, X Wu, Y Chen, Y Gao… - Optical …, 2024 - spiedigitallibrary.org
A lens-free on-chip digital holographic microscope (LFOCDHM) is essential for a variety of
biomedical applications such as cell cycle assays, drug development, digital pathology, and …
biomedical applications such as cell cycle assays, drug development, digital pathology, and …
CSTrans: cross-subdomain transformer for unsupervised domain adaptation
J Liu, X Zhang, Z Luo - Complex & Intelligent Systems, 2025 - Springer
Unsupervised domain adaptation (UDA) aims to make full use of a labeled source domain
data to classify an unlabeled target domain data. With the success of Transformer in various …
data to classify an unlabeled target domain data. With the success of Transformer in various …