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Marko Gruner - IEEE Xplore Author Profile

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Author details

Marko Gruner

Also published under: M. Gruner

Publications

43

Citations

205

Publications by Year

20082024

Co-Authors:

Alessandro AimoneAbderrahmane Ait-OualiSaleem AlreeshDimitris ApostolopoulosNikos Argyris

Show All Co-Authors (104)

Marko Gruner

Also published under: M. Gruner

Affiliation

Fraunhofer HHI, Berlin, Germany

Publication Topics

Optical Power,
Terahertz Bandwidth,
Terahertz Time-domain Spectroscopy,
Dynamic Range,
Emitted Power,
High Dynamic Range,
Optical Pulse,
Terahertz Power,
Terahertz Pulse,
Terahertz Spectra,
Time-domain Spectrometer,
Time-domain Spectroscopy

Biography

Marko Gruner, photograph and biography not available at the time of publication.(Based on document published on 18 November 2016).

Publications

43

Citations

205

Publications by Year

20082024

Co-Authors:

Alessandro Aimone
Abderrahmane Ait-Ouali
Saleem Alreesh
Dimitris Apostolopoulos
Nikos Argyris

Show All Co-Authors (104)

Author's Published Works

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Showing 1-8 of 8 results

Conferences (7)

Journals (1)

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Juan Lyu(8)
Sai Ho Ling(7)
Sunetra Banerjee(5)
Yong-Ping Zheng(4)
De Yang(3)
College of Information and Communication Engineering, Harbin Engineering University, Harbin, China(4)
School of Biomedical Engineering, University of Technology, Sydney, NSW, Australia(3)
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong(3)
School of Biomedical Engineering, University of Technology Sydney, NSW, Australia(2)
Faculty of Information Technology, Monash University, Melbourne, VIC, Australia(1)
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)(3)
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)(1)
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)(1)
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)(1)
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)(1)
IEEE(8)
Berlin, Germany(3)
Honolulu, HI, USA(1)
Montreal, QC, Canada(1)
Nice, France(1)
Toronto, ON, Canada(1)
Convolutional Neural Network(6)
Convolutional Layers(5)
Neural Network(4)
Ultrasound Imaging(4)
3D Ultrasound(3)

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Results

DA-GAN: Learning Structured Noise Removal In Ultrasound Volume Projection Imaging For Enhanced Spine Segmentation

Zixun Huang;Rui Zhao;Frank H. F. Leung;Kin-Man Lam;Sai Ho Ling;Juan Lyu;Sunetra Banerjee;Timothy Tin-Yan Lee;De Yang;Yong-Ping Zheng

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

Year: 2021 | Conference Paper |

Cited by: Papers (3)

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Ultrasound volume projection imaging (VPI) has shown to be appealing from a clinical perspective, because of its harmlessness, flexibility, and efficiency in scoliosis assessment. However, the limitations in hardware devices degrade the resultant image content with strong structured noise. Owing to the unavailability of reference data and the unpredictable degradation model, VPI image recovery is ...Show More

DA-GAN: Learning Structured Noise Removal In Ultrasound Volume Projection Imaging For Enhanced Spine Segmentation

Zixun Huang;Rui Zhao;Frank H. F. Leung;Kin-Man Lam;Sai Ho Ling;Juan Lyu;Sunetra Banerjee;Timothy Tin-Yan Lee;De Yang;Yong-Ping Zheng

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

Year: 2021 | Conference Paper |

Convolutional Neural Networks-Based Lung Nodule Classification: A Surrogate-Assisted Evolutionary Algorithm for Hyperparameter Optimization

Miao Zhang;Huiqi Li;Shirui Pan;Juan Lyu;Steve Ling;Steven Su

IEEE Transactions on Evolutionary Computation

Year: 2021 | Volume: 25, Issue: 5 | Journal Article |

Cited by: Papers (53)

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This article investigates deep neural networks (DNNs)-based lung nodule classification with hyperparameter optimization. Hyperparameter optimization in DNNs is a computationally expensive problem, and a surrogate-assisted evolutionary algorithm has been recently introduced to automatically search for optimal hyperparameter configurations of DNNs, by applying computationally efficient surrogate mod...Show More

Convolutional Neural Networks-Based Lung Nodule Classification: A Surrogate-Assisted Evolutionary Algorithm for Hyperparameter Optimization

Miao Zhang;Huiqi Li;Shirui Pan;Juan Lyu;Steve Ling;Steven Su

IEEE Transactions on Evolutionary Computation

Year: 2021 | Volume: 25, Issue: 5 | Journal Article |

Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise

Zixun Huang;Li-Wen Wang;Frank H. F. Leung;Sunetra Banerjee;De Yang;Timothy Lee;Juan Lyu;Sai Ho Ling;Yong-Ping Zheng

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2020 | Conference Paper |

Cited by: Papers (12)

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3D ultrasound imaging shows great promise for scoliosis diagnosis thanks to its low-costing, radiation-free and real-time characteristics. The key to accessing scoliosis by ultrasound imaging is to accurately segment the bone area and measure the scoliosis degree based on the symmetry of the bone features. The ultrasound images tend to contain many speckles and regular occlusion noise which is dif...Show More

Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise

Zixun Huang;Li-Wen Wang;Frank H. F. Leung;Sunetra Banerjee;De Yang;Timothy Lee;Juan Lyu;Sai Ho Ling;Yong-Ping Zheng

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Year: 2020 | Conference Paper |

Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network

Sunetra Banerjee;Sai Ho Ling;Juan Lyu;Steven Su;Yong-Ping Zheng

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Year: 2020 | Conference Paper |

Cited by: Papers (4)

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Scoliosis is a 3D spinal deformation where the spine takes a lateral curvature, which generates an angle in a coronal plane. For periodic detection of scoliosis, safe and economic imaging modality is needed as continuous exposure to radiative imaging may cause cancer. 3D ultrasound imaging is a cost-effective and radiation-free imaging modality which gives volume projection image. Identification o...Show More

Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network

Sunetra Banerjee;Sai Ho Ling;Juan Lyu;Steven Su;Yong-Ping Zheng

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Year: 2020 | Conference Paper |

Classification of EEG Motor Imagery Tasks Using Convolution Neural Networks

Sai Ho Ling;Henry Makgawinata;Fernando Huerta Monsivais;Andre dos Santos Goncalves Lourenco;Juan Lyu;Rifai Chai

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2019 | Conference Paper |

Cited by: Papers (5)

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Electroencephalograph (EEG) is a highly nonlinear data and very difficult to be classified. The EEG signal is commonly used in the area of Brain-Computer Interface (BCI). The signal can be used as an operative command for directional movements for a powered wheelchair to assist people with disability in performing the daily activity.In this paper, we aim to classify Electroencephalograph EEG signa...Show More

Classification of EEG Motor Imagery Tasks Using Convolution Neural Networks

Sai Ho Ling;Henry Makgawinata;Fernando Huerta Monsivais;Andre dos Santos Goncalves Lourenco;Juan Lyu;Rifai Chai

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2019 | Conference Paper |

3D Ultrasound Spine Image Selection Using Convolution Learning-to-Rank Algorithm

Juan Lyu;Sai Ho Ling;Sunetra Banerjee;Jenny J.Y. Zheng;Ka-Lee Lai;De Yang;Yong-Ping Zheng;Steven Su

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2019 | Conference Paper |

Cited by: Papers (2)

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3D Ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. However, the coronal images from different depths of a 3D ultrasound image have different imaging definitions. So there is a need to select the coronal image that would give the best image definition. Also, manual selection of coronal images is time...Show More

3D Ultrasound Spine Image Selection Using Convolution Learning-to-Rank Algorithm

Juan Lyu;Sai Ho Ling;Sunetra Banerjee;Jenny J.Y. Zheng;Ka-Lee Lai;De Yang;Yong-Ping Zheng;Steven Su

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2019 | Conference Paper |

Lung Nodule Classification using A Novel Two-stage Convolutional Neural Networks Structure’

Yang An;Tianren Hu;Jiaqi Wang;Juan Lyu;Sunetra Banerjee;Sai Ho Ling

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2019 | Conference Paper |

Cited by: Papers (5)

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Lung cancer is one of the most fatal cancers in the world. If the lung cancer can be diagnosed at an early stage, the survival rate of patients post treatment increases dramatically. Computed Tomography (CT) diagram is an effective tool to detect lung cancer. In this paper, we proposed a novel two-stage convolution neural network (2S-CNN) to classify the lung CT images. The structure is composed o...Show More

Lung Nodule Classification using A Novel Two-stage Convolutional Neural Networks Structure’

Yang An;Tianren Hu;Jiaqi Wang;Juan Lyu;Sunetra Banerjee;Sai Ho Ling

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2019 | Conference Paper |

Using Multi-level Convolutional Neural Network for Classification of Lung Nodules on CT images

Juan Lyu;Sai Ho Ling

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2018 | Conference Paper |

Cited by: Papers (34)

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Lung cancer is one of the four major cancers in the world. Accurate diagnosing of lung cancer in the early stage plays an important role to increase the survival rate. Computed Tomography (CT)is an effective method to help the doctor to detect the lung cancer. In this paper, we developed a multi-level convolutional neural network (ML-CNN)to investigate the problem of lung nodule malignancy classif...Show More

Using Multi-level Convolutional Neural Network for Classification of Lung Nodules on CT images

Juan Lyu;Sai Ho Ling

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2018 | Conference Paper |

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