Parallel Deep Learning Algorithms With Hybrid Attention Mechanism for Image Segmentation of Lung Tumors

Hu, He-xuan; Li, Qingqiu; Yong, Zhao; Zhang, Ye

doi:10.1109/tii.2020.3022912

Cited by 84 publications

(32 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the latest methodological improvements in the architecture of CNNs that have contributed to more robust and accurate models include coarse-to-fine cascade of two CNNs [166] to address classimbalance issues; the addition of squeeze-and-excitation (SE)-blocks to allow the network to model the channel and spatial information separately [167], increasing the model capacity; or the implementation of attention mechanisms, which enables the network to focus only on most relevant features [168][169][170].…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Artificial intelligence and machine learning for medical imaging: A technology review

Montero¹,

Javaid²,

Valdés

et al. 2021

Physica Medica

229

View full text Add to dashboard Cite

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.

show abstract

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Artificial intelligence and machine learning for medical imaging: A technology review

Montero¹,

Javaid²,

Valdés

et al. 2021

Physica Medica

229

View full text Add to dashboard Cite

show abstract

“…Therefore, we introduce a new principle into the algorithm that is an attention mechanism. The attention mechanism has been widely applied in the image segmentation [ 51 ], object detection [ 52 ], speech recognition [ 53 ], machine translation [ 54 ], mass customization [ 55 ], chaotic time series forecasting [ 56 ], and so on. However, there are few examples of attention mechanism being applied to the management of CLS, let alone to operational decisions at container terminals.…”

Section: An Attention Mechanism Oriented Hybrid Cnn-rnn Deep Learning Architecturementioning

confidence: 99%

An Attention Mechanism Oriented Hybrid CNN‐RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The booming computational thinking and deep learning make it possible to construct agile, efficient, and robust deep learning-driven decision-making support engine for the operation of container terminal handling systems (CTHSs). Within the conceptual framework of computational logistics, an attention mechanism oriented hybrid convolutional neural network and recurrent neural network deep learning architecture (AMO-HCR-DLA) is proposed technically to predict the container terminal liner handling conditions that mainly include liner handling time (LHT) and total working time of quay crane farm (TWT-QCF) for a calling liner. Consequently, the container terminal oriented logistics generalized computation (CTO-LGC) automation and intelligence are established tentatively by AMO-HCR-DLA. A typical regional container terminal hub of China is selected to design, implement, execute, and evaluate the AMO-HCR-DLA with the actual production data. In the case of severe vibration of LHT and TWT-QCF, while forecasting the handling conditions of 210 ships based on the CTO-LGC running log of four years, the forecasting error of LHT within one hour is more than 97% and that of TWT-QCF within six hours accounts for 89.405%. When predicting the operating conditions of 300 liners by the log of five years, the forecasting deviation of LHT within one hour is more than striking 99% and that of TWT-QCF within six hours reaches up to 94.010% as well. All are far superior to the predicting outcomes by the classical algorithms of machine learning and deep learning. Hence, the AMO-HCR-DLA shows excellent performance for the prediction of CTHS with the low and stable computational consuming. It also demonstrates the feasibility, credibility, and realizability of the computing architecture and design paradigm of AMO-HCR-DLA preliminarily.

show abstract

“…The performance of each model was evaluated in terms of accuracy (ACC), F1 score, and Area Under Curve (AUC) -common indicators for machine learning model evaluation (Choubin et al 2019;Hu et al 2021). TP, TN, FP, and FN were true positive (correctly classified flooded sites), true negative (correctly classified non-flooded sites), false positive (misclassified flooded sites) and false negative (misclassified nonflooded sites), respectively.…”

Section: Performance Evaluationmentioning

confidence: 99%

Multiple Kernel Learning with Maximum Inundation Extent from MODIS Imagery for Spatial Prediction of Flood Susceptibility

Zhu

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

This paper proposes a new technology of spatial prediction for flood susceptibility. Multiple kernel learning was used to build the flood susceptibility model and predict the flood inundation risk of the Sanhuajian Basin of the Yellow River. Based on the historical flow records of the Huayuankou Site and the MODIS remote sensing images of the study area, the maximum inundation range was extracted by the open water likelihood index method, and the flooded and non-flooded sample sites were selected. Considering the availability of pertinent literatures and data, ten flood conditioning factors were defined as the sample characteristics. The model performance was evaluated in terms of accuracy, F1 score, and AUC. According to the results, multiple kernel learning significantly outperforms the support vector machine adopting single kernel, and NLMKL demonstrates the best comprehensive performance. The flood susceptibility map generated by MODIS remote sensing images and multiple kernel learning, therefore, can provide effective help for researchers and decision makers in flood management.

show abstract

Parallel Deep Learning Algorithms With Hybrid Attention Mechanism for Image Segmentation of Lung Tumors

Cited by 84 publications

References 21 publications

Artificial intelligence and machine learning for medical imaging: A technology review

Artificial intelligence and machine learning for medical imaging: A technology review

An Attention Mechanism Oriented Hybrid CNN‐RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction

Multiple Kernel Learning with Maximum Inundation Extent from MODIS Imagery for Spatial Prediction of Flood Susceptibility

Contact Info

Product

Resources

About