A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing
Accurate and timely predicting citywide traffic crowd flows precisely is crucial for public
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks
Traffic flow prediction is crucial for public safety and traffic management, and remains a big
challenge because of many complicated factors, eg, multiple spatio-temporal dependencies …
challenge because of many complicated factors, eg, multiple spatio-temporal dependencies …
Multi-scale attention network for diabetic retinopathy classification
MT Al-Antary, Y Arafa - IEEE Access, 2021 - ieeexplore.ieee.org
Diabetic Retinopathy (DR) is a highly prevalent complication of diabetes mellitus, which
causes lesions on the retina that affect vision which may lead to blindness if it is not detected …
causes lesions on the retina that affect vision which may lead to blindness if it is not detected …
LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation
While deep learning methods hitherto have achieved considerable success in medical
image segmentation, they are still hampered by two limitations:(i) reliance on large-scale …
image segmentation, they are still hampered by two limitations:(i) reliance on large-scale …
A review of nuclei detection and segmentation on microscopy images using deep learning with applications to unbiased stereology counting
The detection and segmentation of stained cells and nuclei are essential prerequisites for
subsequent quantitative research for many diseases. Recently, deep learning has shown …
subsequent quantitative research for many diseases. Recently, deep learning has shown …
Dsal: Deeply supervised active learning from strong and weak labelers for biomedical image segmentation
Image segmentation is one of the most essential biomedical image processing problems for
different imaging modalities, including microscopy and X-ray in the Internet-of-Medical …
different imaging modalities, including microscopy and X-ray in the Internet-of-Medical …
A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection
Structural damage detection has become an interdisciplinary area of interest for various
engineering fields, while the available damage detection methods are being in the process …
engineering fields, while the available damage detection methods are being in the process …
Texture attention network for diabetic retinopathy classification
MD Alahmadi - IEEE Access, 2022 - ieeexplore.ieee.org
Diabetic Retinopathy (DR) is a disease caused by a high level of glucose in retina vessels.
This malicious disease put millions of people around the world at risk for vision loss each …
This malicious disease put millions of people around the world at risk for vision loss each …
Mt-uda: Towards unsupervised cross-modality medical image segmentation with limited source labels
The success of deep convolutional neural networks (DCNNs) benefits from high volumes of
annotated data. However, annotating medical images is laborious, expensive, and requires …
annotated data. However, annotating medical images is laborious, expensive, and requires …
Meta-hallucinator: Towards few-shot cross-modality cardiac image segmentation
Abstract Domain shift and label scarcity heavily limit deep learning applications to various
medical image analysis tasks. Unsupervised domain adaptation (UDA) techniques have …
medical image analysis tasks. Unsupervised domain adaptation (UDA) techniques have …