Results 61 to 70 of about 384,232 (160)
Multifiber Array‐Based Photometry System for Multiregional Functional Mapping in the Mouse Brain
Existing fiber photometry approaches suffer from invasiveness and limited scalability. A newly developed multifiber array‐based photometry system allows targeting multiple brain regions with less invasiveness. The system was validated in two jGCaMP8s‐expressing mouse lines by monitoring GABAergic neural population activity across multiple brain regions
Manil Bradai +4 more
wiley +1 more source
Advancing Photonic Inverse Design with Interpretable Machine Learning
The work applies the interpretable machine learning technique called LIME (local interpretable model‐agnostic explanations) to the inverse design of photonic chips, revealing hidden optimization patterns and guiding better starting designs. Using insights from LIME improves performance of two‐mode multiplexers, showing interpretable methods can ...
Lirandë Pira +5 more
wiley +1 more source
Tablet‐based handwriting tasks (spiral, meander, and wave) are transformed into unified images and analyzed using PD‐MGMA‐DSCNN, a lightweight multiscale gated attention network. Bayesian–genetic optimization improves performance, while SHAP attribution maps provide interpretable handwriting biomarkers for Parkinson's disease screening.
Khosro Rezaee, Ali Khalili Fakhrabadi
wiley +1 more source
Hierarchical Superpixel Segmentation via Structural Information Theory
Superpixel segmentation is a foundation for many higher-level computer vision tasks, such as image segmentation, object recognition, and scene understanding.
Wu, Jia +7 more
core +4 more sources
Pupil Plane Multiplexing for Vectorial Fourier Ptychography
This study proposes a cost‐effective, modality‐adaptive multichannel microscopy framework using pupil‐plane multiplexing. A custom pupil aperture at the Fourier plane encodes channel‐specific transfer functions with spectral or polarization filters, and model‐based reconstruction with channel‐dependent priors decodes them.
Hyesuk Chae +5 more
wiley +1 more source
In the field of remote sensing, using a large amount of labeled image data to supervise the training of fully convolutional networks for the semantic segmentation of images is expensive.
Zenan Yang +4 more
doaj +1 more source
This study presents an interpretable, lightweight hybrid deep learning model for real‐time analysis of breast cancer histopathology in IoMT‐enabled diagnostic systems. By integrating MobileNetV2 and EfficientNet‐B0 with a novel contextual recurrent attention module (CRAM), the framework achieves near‐perfect accuracy while providing transparent Grad ...
Roseline Oluwaseun Ogundokun +4 more
wiley +1 more source
Abstract Background Accurate classification of brain tumors is a major challenge in neuro‐oncology, as the heterogeneity of tumor morphology and the overlap of radiological features limit the effectiveness of conventional diagnostic approaches. Early and reliable tumor characterization is essential for treatment planning, prognosis, and improved ...
Mus'ab S. Alkasasbeh +7 more
wiley +1 more source
A conditional multi‐task deep learning framework is developed for designing and optimizing Full‐Stokes Hyperspectro‐Polarimetric Encoding Metasurfaces (FHPEMs). This framework achieves joint spectro‐polarimetric learning and unified forward–inverse design.
Chenjie Gong +9 more
wiley +1 more source
An Object-Aware Network Embedding Deep Superpixel for Semantic Segmentation of Remote Sensing Images
Semantic segmentation forms the foundation for understanding very high resolution (VHR) remote sensing images, with extensive demand and practical application value.
Ziran Ye +5 more
doaj +1 more source

