An unsupervised-learning-based approach for automated defect inspection on textured surfaces
S Mei, H Yang, Z Yin - IEEE transactions on instrumentation …, 2018 - ieeexplore.ieee.org
Automated defect inspection has long been a challenging task especially in industrial
applications, where collecting and labeling large amounts of defective samples are usually …
applications, where collecting and labeling large amounts of defective samples are usually …
A healthy dose of chaos: Using fractal frameworks for engineering higher-fidelity biomedical systems
Optimal levels of chaos and fractality are distinctly associated with physiological health and
function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit …
function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit …
Multiscale feature-clustering-based fully convolutional autoencoder for fast accurate visual inspection of texture surface defects
Visual inspection of texture surface defects is still a challenging task in the industrial
automation field due to the tremendous changes in the appearance of various surface …
automation field due to the tremendous changes in the appearance of various surface …
A novel method based on deep convolutional neural networks for wafer semiconductor surface defect inspection
G Wen, Z Gao, Q Cai, Y Wang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Semiconductor wafer is widely used in welding robot, spray robot, unmanned material
delivery vehicle, and detection station sensor. The defects of semiconductor wafer, such as …
delivery vehicle, and detection station sensor. The defects of semiconductor wafer, such as …
A feature memory rearrangement network for visual inspection of textured surface defects toward edge intelligent manufacturing
Recent advances in the industrial inspection of textured surfaces—in the form of visual
inspection—have made such inspections possible for efficient, flexible manufacturing …
inspection—have made such inspections possible for efficient, flexible manufacturing …
The utility of MRI histogram and texture analysis for the prediction of histological diagnosis in head and neck malignancies
N Fujima, A Homma, T Harada, Y Shimizu, KK Tha… - Cancer Imaging, 2019 - Springer
Background To assess the utility of histogram and texture analysis of magnetic resonance
(MR) fat-suppressed T2-weighted imaging (Fs-T2WI) for the prediction of histological …
(MR) fat-suppressed T2-weighted imaging (Fs-T2WI) for the prediction of histological …
Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients
Purpose Neoadjuvant chemotherapy (NAC) is used to treat patients with high-risk breast
cancer. The tumor response to NAC can be classified as either a pathological partial …
cancer. The tumor response to NAC can be classified as either a pathological partial …
Computational quantitative MR image features-a potential useful tool in differentiating glioblastoma from solitary brain metastasis
K Petrujkić, N Milošević, N Rajković… - European Journal of …, 2019 - Elsevier
Purpose Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors
in the adult population. Their presentation on conventional MRI is quite similar, but treatment …
in the adult population. Their presentation on conventional MRI is quite similar, but treatment …
Cloud/snow recognition for multispectral satellite imagery based on a multidimensional deep residual network
M Xia, W Liu, B Shi, L Weng, J Liu - International journal of remote …, 2019 - Taylor & Francis
Cloud/snow recognition technology for multispectral satellite imagery plays an important role
in resource investigation, natural disasters, and environmental pollution. Traditional feature …
in resource investigation, natural disasters, and environmental pollution. Traditional feature …
Machine-learning-based prediction of treatment outcomes using MR imaging-derived quantitative tumor information in patients with sinonasal squamous cell …
N Fujima, Y Shimizu, D Yoshida, S Kano, T Mizumachi… - Cancers, 2019 - mdpi.com
The purpose of this study was to determine the predictive power for treatment outcome of a
machine-learning algorithm combining magnetic resonance imaging (MRI)-derived data in …
machine-learning algorithm combining magnetic resonance imaging (MRI)-derived data in …