RETRACTED ARTICLE: IoT-based 3D convolution for video salient object detection

Dong, Shizhou; Gao, Zhifan; Pirbhulal, Sandeep; Bian, Gui‐Bin; Zhang, Heye; Li, Shuo

doi:10.1007/s00521-018-03971-3

Cited by 27 publications

(7 citation statements)

References 37 publications

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“…Even a professional doctor takes a long time to build one patient's manual segmentation. In addition, compared with other computer vision tasks (58,59), extremely high precision and detail preservation hold higher stakes for medical image segmentation, as inaccuracies in NPC segmentation may lead to damages of the patient's brain.…”

Section: Discussionmentioning

confidence: 99%

AttR2U-Net: A Fully Automated Model for MRI Nasopharyngeal Carcinoma Segmentation Based on Spatial Attention and Residual Recurrent Convolution

Zhang

Han

et al. 2022

Front. Oncol.

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Radiotherapy is an essential method for treating nasopharyngeal carcinoma (NPC), and the segmentation of NPC is a crucial process affecting the treatment. However, manual segmentation of NPC is inefficient. Besides, the segmentation results of different doctors might vary considerably. To improve the efficiency and the consistency of NPC segmentation, we propose a novel AttR2U-Net model which automatically and accurately segments nasopharyngeal carcinoma from MRI images. This model is based on the classic U-Net and incorporates advanced mechanisms such as spatial attention, residual connection, recurrent convolution, and normalization to improve the segmentation performance. Our model features recurrent convolution and residual connections in each layer to improve its ability to extract details. Moreover, spatial attention is fused into the network by skip connections to pinpoint cancer areas more accurately. Our model achieves a DSC value of 0.816 on the NPC segmentation task and obtains the best performance compared with six other state-of-the-art image segmentation models.

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Section: Discussionmentioning

confidence: 99%

AttR2U-Net: A Fully Automated Model for MRI Nasopharyngeal Carcinoma Segmentation Based on Spatial Attention and Residual Recurrent Convolution

Zhang

Han

et al. 2022

Front. Oncol.

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“…However, the model is shallow, making the inferred networks may not be as faithful as those inferred from deep models. We believe that many promising deep-learning-based methods (Cui et al 2018;Zhao et al 2018;Dong et al 2019) deserve more efforts.…”

Section: Limitationsmentioning

confidence: 99%

Cortical 3-hinges could serve as hubs in cortico-cortical connective network

Zhang

Jiang

et al. 2020

Brain Imaging and Behavior

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Mapping the relation between cortical convolution and structural/functional brain architectures could provide deep insights into the mechanisms of brain development, evolution and diseases. In our previous studies, we found a unique gyral folding pattern, termed a 3-hinge, which was defined as the conjunction of three gyral crests. The uniqueness of the 3-hinge was evidenced by its thicker cortex and stronger fiber connections than other gyral regions. However, the role that 3-hinges play in cortico-cortical connective architecture remains unclear. To this end, we conducted MRI studies by constructing structural cortico-cortical connective networks based on a fine-granular cortical parcellation, the parcels of which were automatically labeled as 3-hinge, 2-hinge (ordinary gyrus) or sulcus. On human brains, 3-hinges possess significantly higher degrees, strengths and betweennesses than 2-hinges, suggesting that 3-hinges could serve more like hubs in the cortico-cortical connective network. This hypothesis gains supports from human functional network analyses, in which 3-hinges are involved in more global functional networks than ordinary gyri. In addition, 3-hinges could serve as ‘connector’ hubs rather than ‘provincial’ hubs and they account for a dominant proportion of nodes in the high-level ‘backbone’ of the network. These structural results are reproduced on chimpanzee and macaque brains, while the roles of 3-hinges as hubs become more pronounced in higher order primates. Our new findings could provide a new window to the relation between cortical convolution, anatomical connection and brain function.

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“…With the development of the neural network, more and more new architectures have been proposed. 3D convolution structure is added to model the motion information of objects [52]. Semisupervised adversarial learning method is recently used in salient object detection [53], [54].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Iris Tracking Using Deep Regression Networks for Robotic Ophthalmic Surgery

Qiu

Yang

et al. 2020

IEEE Access

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Robotic-assisted platforms are expected to guarantee the accuracy of surgical operation and accelerate its learning curve. Iris tracking can guide the robotic manipulator during the operation. However, few researches focused on it during surgery. It is a big challenge due to the deformation of the iris and occlusion caused by instruments. A novel real-time iris tracking method based on a regression network are proposed to meet the speed and accuracy requirements of the ophthalmic robotic system. It utilizes the low-level visual features and high-level semantic meanings from different layers to capture the discriminative representation of the iris target. Then the bottleneck layers are added to improve computation efficiency. Furthermore, a multi-loss function is designed by jointly learning Absolute loss and Euclidean loss. Finally, the experimental results under the typical surgical scene demonstrate that iris tracker achieves an accuracy of 89.16% and a real-time speed of 134fps with GPU, which is suitable for the ophthalmic robotic system to perform real-time robotic manipulation. INDEX TERMS Robotic surgery, deep learning, cataract surgery, iris tracking, real-time tracking.

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RETRACTED ARTICLE: IoT-based 3D convolution for video salient object detection

Cited by 27 publications

References 37 publications

AttR2U-Net: A Fully Automated Model for MRI Nasopharyngeal Carcinoma Segmentation Based on Spatial Attention and Residual Recurrent Convolution

AttR2U-Net: A Fully Automated Model for MRI Nasopharyngeal Carcinoma Segmentation Based on Spatial Attention and Residual Recurrent Convolution

Cortical 3-hinges could serve as hubs in cortico-cortical connective network

Real-Time Iris Tracking Using Deep Regression Networks for Robotic Ophthalmic Surgery

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