Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)scans.These factors present significant challenges for MRI-based segmentation,a...Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)scans.These factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma progression.This study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation accuracy.ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms.This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range dependencies.By doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor boundaries.We rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 datasets.Notably,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse datasets.Furthermore,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset size.Radiomic features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival prediction.This model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing methods.This ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient survival.Importantly,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.展开更多
The routing problem for intelligent and connected vehicles has garnered significant attention because of its profound theoretical implications and wide-ranging practical applications.Despite advancements,existing lear...The routing problem for intelligent and connected vehicles has garnered significant attention because of its profound theoretical implications and wide-ranging practical applications.Despite advancements,existing learning-based methods often rely on training one single policy,which inadequately explores the solution space and leads to suboptimal performance.To address this limitation,we propose a diversified tour-driven deep reinforcement learning(DT-DRL)approach for solving vehicle routing problems(VRPs)across various scales.Our approach builds on the encoder‒decoder paradigm,with the encoder utilizing a multihead attention mechanism to derive informative node embeddings and a gate aggregation block to enhance state representation.During decoding,dynamic-aware context embedding is designed to capture real-time state transitions and graph variations,thereby offering comprehensive and timely information for decision-making.To promote solution diversity and expand the search space,multiple decoders with independent parameters are employed,coupled with a Kullback-Leibler divergencebased cross-entropy loss that regularizes the generation of diversified candidate tours.We validate the proposed DT-DRL through extensive experimentation on two representative routing problems for intelligent connected vehicles,namely,the traveling salesman problem(TSP)and the capacitated VRP(CVRP).The results demonstrate that DT-DRL consistently outperforms many heuristic and DRL-based methods,achieving up to a 7.54%improvement in the optimality gap,thereby establishing its effectiveness and robustness in tackling complex routing challenges for intelligent and connected vehicles.展开更多
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through a Large Research Project under grant number RGP2/254/45.
文摘Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)scans.These factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma progression.This study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation accuracy.ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms.This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range dependencies.By doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor boundaries.We rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 datasets.Notably,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse datasets.Furthermore,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset size.Radiomic features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival prediction.This model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing methods.This ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient survival.Importantly,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.
基金supported in part by the National Natural Science Foundation of China(No.62306232)the National Key R&D Program of China(No.2021YFB2401900)+1 种基金the Natural Science Basic Research Program of Shaanxi Province(No.2023-JC-QN-0662)the State Key Laboratory of Electrical Insulation and Power Equipment(No.EIPE23416).
文摘The routing problem for intelligent and connected vehicles has garnered significant attention because of its profound theoretical implications and wide-ranging practical applications.Despite advancements,existing learning-based methods often rely on training one single policy,which inadequately explores the solution space and leads to suboptimal performance.To address this limitation,we propose a diversified tour-driven deep reinforcement learning(DT-DRL)approach for solving vehicle routing problems(VRPs)across various scales.Our approach builds on the encoder‒decoder paradigm,with the encoder utilizing a multihead attention mechanism to derive informative node embeddings and a gate aggregation block to enhance state representation.During decoding,dynamic-aware context embedding is designed to capture real-time state transitions and graph variations,thereby offering comprehensive and timely information for decision-making.To promote solution diversity and expand the search space,multiple decoders with independent parameters are employed,coupled with a Kullback-Leibler divergencebased cross-entropy loss that regularizes the generation of diversified candidate tours.We validate the proposed DT-DRL through extensive experimentation on two representative routing problems for intelligent connected vehicles,namely,the traveling salesman problem(TSP)and the capacitated VRP(CVRP).The results demonstrate that DT-DRL consistently outperforms many heuristic and DRL-based methods,achieving up to a 7.54%improvement in the optimality gap,thereby establishing its effectiveness and robustness in tackling complex routing challenges for intelligent and connected vehicles.
