Results 61 to 70 of about 4,886 (174)

Interference by Design: Embedding Moiré Beat‐Field Geometry into Variational Quantum Circuits for Brain Tumour Diagnosis

Advanced Quantum Technologies, Volume 9, Issue 10, October 2026.
What if the key to efficient quantum diagnosis lies not in the circuit, but in the geometry feeding it? Embedding Moiré beat‐field interference into a variational quantum circuit sharpens brain tumour class boundaries, lifting four‐class MRI accuracy to 92.5% and halving circuit deptheach claim validated on real quantum hardware.
Akoramurthy B, Surendiran B, Xiaochun Cheng   +2 more
wiley   +1 more source

AI‐Powered TAT‐Net Hyperspectral Imaging for Rapid and Accurate Differentiation of Sinonasal Inverted Papilloma and Malignancy

World Journal of Otorhinolaryngology - Head and Neck Surgery, Volume 12, Issue 5, Page 611-619, October 2026.
ABSTRACT Background Sinonasal inverted papilloma (IP) and malignancies share overlapping clinical and endoscopic features, and conventional histopathology frequently requires time‐consuming adjunct immunohistochemistry. Hyperspectral imaging (HSI) can capture subtle spectral changes associated with malignant transformation that are not apparent on ...
Hao‐Miao Zhao, Jia‐Chang Kong, Yun‐Ze Li, Wei Li, Xin Feng   +4 more
wiley   +1 more source

Designing Lightweight Feature Descriptor Networks with Depthwise Separable Convolution

, 2021
Extracting feature points and their descriptors from images is one of the fundamental techniques in computer vision with many applications such as geometric fitting and camera calibration, and for this task several deep learning models have been proposed.
WANG, Yeo Ree, KANEMURA, Atsunori
openaire   +2 more sources

Dissecting Glioma Heterogeneity: A Deep Hybrid Graph Convolutional Network With Hinge Attention for Causal‐Effect Explainability

CAAI Transactions on Intelligence Technology, Volume 11, Issue 5, Page 1413-1429, October 2026.
ABSTRACT Brain tumour classification is a critical task in medical imaging that requires accurate and interpretable solutions to assist in clinical decision‐making. In this paper, we present GraphConvNet‐X, a novel hybrid model that integrates convolutional neural networks (CNNs) for spatial feature extraction with graph neural networks (GNNs) that ...
Sultanul Arifeen Hamim, Mubasshar U. I. Tamim, S. M. Hasan Mahmud, M. F. Mridha, Zeyar Aung   +4 more
wiley   +1 more source

A Review and Prospect of Appearance Defect Detection Methods for High Voltage Equipment Based on Vision Large Models

High Voltage, Volume 11, Issue 5, Page 1664-1684, October 2026.
ABSTRACT High voltage equipment, as a core component of power systems, plays an indispensable role in ensuring the reliability of power supply through its safe and stable operation. Traditional visual defect detection for high voltage equipment, however, often relies on manual inspection and experience‐based judgement, which struggles to meet the ...
Zhenbing Zhao, Shuo Feng, Teng Ma, Chenkang Tang, Wenlong Liao, Yixuan Huang, Yongjie Zhai, Wenqing Zhao   +7 more
wiley   +1 more source

Deep Learning Predicts Hematopoietic Stem Cell Aging From 3D Chromatin Images

Aging Cell, Volume 25, Issue 10, October 2026.
Deep learning identifies age‐associated chromatin architecture signatures in 3D images of hematopoietic stem cell nuclei, providing an interpretable imaging biomarker that distinguishes young from aged cells and detects chromatin rejuvenation after treatment.
Pablo Iáñez Picazo, Eva Mejía‐Ramírez, Dario Di Bari, Elena Vitali, Maria Carolina Florian, Paula Petrone   +5 more
wiley   +1 more source

DTPP:An efficient depthwise separable TCN for seismic phase picking

Artificial Intelligence in Geosciences
With the rapid development of artificial intelligence in seismology, various deep learning-based seismic phase picking models have emerged in recent years.
Shuai Lv, Yuxiang Peng
doaj   +1 more source

A Memristor‐Based In‐Memory Computing System‐on‐Chip with Efficient Depthwise Convolution

Advanced Intelligent Systems, Volume 8, Issue 9, September 2026.
We present a memristor‐based in‐memory computing (IMC) architecture that enables efficient depthwise convolution (DWC) acceleration. Fabricated in a system‐on‐chip with crossbar arrays, the design improves memory utilization. Experimental validation demonstrates the first hardware acceleration of DWC in IMC, achieving a digital comparable inference ...
Wenhao Song, Fuxi Cai, Sangsu Park, Zhaoyan Li, Seonghun Lee, Mingyi Rao, Woohyung Choi, Seungin Shin, Yan Zhu, Sujin Chae, Myoungsub Kim, Mingche Wu, Hyejung Choi, Yuan Dao, Jaeyun Yi, Youngman Cho, Seon Yong Cha, Ning Ge, Qiangfei Xia, Soo Gil Kim, Miao Hu, J. Joshua Yang   +21 more
wiley   +1 more source

A Deep Network Incorporating Depthwise Separable Convolutions for Pathological Diagnosis of Chest X‐Ray Images: A Model Development and Validation Study

Health Science Reports
Background Pathological diagnosis of chest X‐ray images has always been a very challenging subject. Methods We propose a chest X‐ray pathology detection network that fuses two depthwise separable convolutions (TDCheXNet) to detect pathology from chest X ...
Na Zhang, Guanghong Deng, Wenlong Jing, Yong Li, Li Li   +4 more
doaj   +1 more source

Short-time photovoltaic output prediction method based on depthwise separable convolution Visual Geometry group- deep gate recurrent neural network

Frontiers in Energy Research
In response to the issue of short-term fluctuations in photovoltaic (PV) output due to cloud movement, this paper proposes a method for forecasting short-term PV output based on a Depthwise Separable Convolution Visual Geometry Group (DSCVGG) and a Deep ...
Lei Zhang, Shuang Zhao, Guanchao Zhao, Lingyi Wang, Baolin Liu, Zhimin Na, Zhijian Liu, Zhongming Yu, Wei He   +8 more
doaj   +1 more source