SeqSeg: A sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance

[1]  Yan Wang,et al.  DA-DSUnet: Dual Attention-based Dense SU-net for automatic head-and-neck tumor segmentation in MRI images , 2021, Neurocomputing.

[2]  Zhiyao Wen,et al.  A Comprehensive Review of Deep Reinforcement Learning for Object Detection , 2021, 2021 International Symposium on Artificial Intelligence and its Application on Media (ISAIAM).

[3]  Xingchen Peng,et al.  The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods , 2019, Technology in cancer research & treatment.

[4]  Hongmin Cai,et al.  Achieving Accurate Segmentation of Nasopharyngeal Carcinoma in MR Images Through Recurrent Attention , 2019, MICCAI.

[5]  Fei Wu,et al.  Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net , 2019, IEEE Transactions on Medical Imaging.

[6]  Pheng-Ann Heng,et al.  Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma. , 2019, Radiology.

[7]  Ying Sun,et al.  Radiomics on multi-modalities MR sequences can subtype patients with non-metastatic nasopharyngeal carcinoma (NPC) into distinct survival subgroups , 2019, European Radiology.

[8]  Jianxin Wang,et al.  Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality MRI , 2019, Neural Computing and Applications.

[9]  Silvio Savarese,et al.  Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Lijun Zhao,et al.  Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images , 2019, Journal of Digital Imaging.

[11]  Yong Yin,et al.  MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma , 2018, Neurocomputing.

[12]  Qiaoliang Li,et al.  Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study , 2018, Contrast media & molecular imaging.

[13]  Qiaoliang Li,et al.  Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network , 2018, BioMed research international.

[14]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Xiaojun Chang,et al.  Reinforcement Cutting-Agent Learning for Video Object Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[16]  Loïc Le Folgoc,et al.  Attention U-Net: Learning Where to Look for the Pancreas , 2018, ArXiv.

[17]  Jiliu Zhou,et al.  Automatic Tumor Segmentation with Deep Convolutional Neural Networks for Radiotherapy Applications , 2018, Neural Processing Letters.

[18]  Tao Zhang,et al.  Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images , 2017, Front. Oncol..

[19]  Philip Bachman,et al.  Deep Reinforcement Learning that Matters , 2017, AAAI.

[20]  Gang Sun,et al.  Squeeze-and-Excitation Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[21]  J. Hodgins,et al.  Learning to Schedule Control Fragments for Physics-Based Characters Using Deep Q-Learning , 2017, ACM Trans. Graph..

[22]  Hao Chen,et al.  VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images , 2017, NeuroImage.

[23]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[24]  S. Yom,et al.  Reducing radiation-related morbidity in the treatment of nasopharyngeal carcinoma. , 2017, Future oncology.

[25]  Serge J. Belongie,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Yoshua Bengio,et al.  The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[27]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Liu Chen,et al.  Nasopharyngeal carcinoma segmentation via HMRF-EM with maximum entropy , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[30]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[32]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[33]  Trevor Darrell,et al.  Fully convolutional networks for semantic segmentation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[35]  Wen-Chen Huang,et al.  A hybrid supervised learning nasal tumor discrimination system for DMRI , 2012 .

[36]  O. Commowick,et al.  A pre-clinical assessment of an atlas-based automatic segmentation tool for the head and neck. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[37]  Pengfei Xu,et al.  Nasopharyngeal carcinoma lesion segmentation from MR images by support vector machine , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[38]  J. Sham,et al.  Nasopharyngeal carcinoma , 2005, The Lancet.

[39]  A. King,et al.  Neck node metastases from nasopharyngeal carcinoma: MR imaging of patterns of disease , 2000, Head & neck.

[40]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[41]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[42]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.