RETRACTED: ARM-Net: Improved MRI brain tumor segmentation method based on attentional mechanism and residual module
DOI:
https://doi.org/10.4108/eetel.5953Keywords:
Brain tumor segmentation, Encoder decoder, Attention mechanism, Residual moduleAbstract
RETRACTED: The article has been retracted due to misconduct during the peer review process. The retraction notice can be found here: https://doi.org/10.4108/eetel.12220
References
[1] KRISHNAPRIYA S, KARUNA Y. A survey of deep learning for MRI brain tumor segmentation methods: Trends, challenges, and future directions [J]. Health and Technology, 2023, 13(2): 181-201.
[2] MOHAMMED Y M, EL GAROUANI S, JELLOULI I. A survey of methods for brain tumor segmentation-based MRI images [J]. Journal of Computational Design and Engineering, 2023, 10(1): 266-93.
[3] FARAJZADEH N, SADEGHZADEH N, HASHEMZADEH M. Brain tumor segmentation and classification on MRI via deep hybrid representation learning [J]. Expert Systems with Applications, 2023, 224: 119963.
[4] LIU Z, TONG L, CHEN L, et al. Deep learning based brain tumor segmentation: a survey [J]. Complex & intelligent systems, 2023, 9(1): 1001-26.
[5] KUMAR A. Study and analysis of different segmentation methods for brain tumor MRI application [J]. Multimedia Tools and Applications, 2023, 82(5): 7117-39.
[6] FANG L, WANG X. Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation [J]. Biomedical Signal Processing and Control, 2023, 79: 104027.
[7] AGGARWAL M, TIWARI A K, SARATHI M P, et al. An early detection and segmentation of Brain Tumor using Deep Neural Network [J]. BMC Medical Informatics and Decision Making, 2023, 23(1): 78.
[8] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 3431-40.
[9] RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation[C]//Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. 2015: 234-41.
[10] ZHOU Z, SIDDIQUEE M M R, TAJBAKHSH N, et al. Unet++: Redesigning skip connections to exploit multiscale features in image segmentation [J]. IEEE transactions on medical imaging, 2019, 39(6): 1856-67.
[11] HUANG H, LIN L, TONG R, et al. Unet 3+: A full-scale connected unet for medical image segmentation[C]//ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). 2020: 1055-9.
[12] JIANG Z, DING C, LIU M, et al. Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task[C]//Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I 5. 2020: 231-41.
[13] MYRONENKO A. 3D MRI brain tumor segmentation using autoencoder regularization[C]//Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4. 2019: 311-20.
[14] ALLAH A M G, SARHAN A M, ELSHENNAWY N M. Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information [J]. Expert Systems with Applications, 2023, 213: 118833.
[15] CAO Y, ZHOU W, ZANG M, et al. MBANet: A 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images [J]. Biomedical Signal Processing and Control, 2023, 80: 104296.
[16] ZHU Z, HE X, QI G, et al. Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI [J]. Information Fusion, 2023, 91: 376-87.
[17] RUAN J, XIANG S, XIE M, et al. MALUNet: A multi-attention and light-weight unet for skin lesion segmentation[C]//2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2022: 1150-6.
[18] IBTEHAZ N, RAHMAN M S. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation [J]. Neural networks, 2020, 121: 74-87.
[19] HASHEMI N, MASOUDNIA S, NEJAD A, et al. A memory-efficient deep framework for multi-modal mri-based brain tumor segmentation[C]//2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022: 3749-52.
[20] MAO A, MOHRI M, ZHONG Y. Cross-entropy loss functions: Theoretical analysis and applications[C]//International Conference on Machine Learning. 2023: 23803-28.
[21] NOORI M, BAHRI A, MOHAMMADI K. Attention-guided version of 2D UNet for automatic brain tumor segmentation[C]//2019 9th international conference on computer and knowledge engineering (ICCKE). 2019: 269-75.
[22] SOLTANI-GOL M, FATTAHI M, SOLTANIAN-ZADEH H, et al. DRAU-Net: Double Residual Attention Mechanism for automatic MRI brain tumor segmentation[C]//2022 30th International Conference on Electrical Engineering (ICEE). 2022: 587-91.
[23] LI H, JIANG G, ZHANG J, et al. Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images [J]. NeuroImage, 2018, 183: 650-65.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on e-Learning
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
