Large-Kernel Attention for 3D Medical Image Segmentation

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660.

Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA: IEEE; 2015. p. 3431–40.

Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, vol. 9351. Cham: Springer International Publishing; 2015. p. 234–41.

Chapter  Google Scholar 

Chen J, Zhang H, Mohiaddin R, Wong T, Firmin D, Keegan J, et al. Adaptive hierarchical dual consistency for semi-supervised left atrium segmentation on cross-domain data. IEEE Trans Med Imag. 2021;41(2):420–33.

Article  Google Scholar 

Li H, Nan Y, DelSer J, Yang G. Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation. Neural Comput Appl. 2022;1–15.

Li H, Tang Z, Nan Y. Yang G. Human treelike tubular structure segmentation: a comprehensive review and future perspectives. Comput Biol Med. 2022;106241.

Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, et al. Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int J Comput Assist Radiol Surg. 2017;12(2):183–203. https://doi.org/10.1007/s11548-016-1483-3.

DSouza AM, Chen L, Wu Y, Abidin AZ, Xu C, Wismüller A. MRI tumor segmentation with densely connected 3D CNN. In: Angelini ED, Landman BA, editors. Medical Imaging 2018: Image Processing. Houston, United States: SPIE; 2018. p. 50.

Jia H, Cai W, Huang H, Xia Y. H2NF-Net for brain tumor segmentation using multimodal MR imaging: 2nd place solution to BraTS challenge 2020 segmentation task. In: Crimi A, Bakas S, editors. Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries, vol. 12659. Cham: Springer International Publishing; 2021. p. 58–68.

Chapter  Google Scholar 

Lindsay GW. Attention in psychology, neuroscience, and machine learning. Front Comput Neurosci. 2020;14.

Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, et al. TransUNet: Transformers make strong encoders for medical image segmentation. arXiv:2102.04306 [Preprint]. 2021. Available from: http://arxiv.org/abs/2102.04306.

Sinha A, Dolz J. Multi-scale self-guided attention for medical image segmentation. IEEE J Biomed Health Inform. 2021;25(1):121–30. https://doi.org/10.1109/JBHI.2020.2986926.

Valanarasu JMJ, Oza P, Hacihaliloglu I, Patel VM, et al. Medical transformer: Gated axial-attention for medical image segmentation. In: de Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, et al., editors. Medical image computing and computer assisted intervention - MICCAI 2021, vol. 12901. Cham: Springer International Publishing; 2021. p. 36–46.

Chapter  Google Scholar 

Guo MH, Xu TX, Liu JJ, Liu ZN, Jiang PT, Mu TJ, et al. Attention mechanisms in computer vision: a survey. Comput Visual Media. 2022. https://doi.org/10.1007/s41095-022-0271-y.

Woo S, Park J, Lee JY, Kweon IS. CBAM: Convolutional block attention module. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y, editors. Computer Vision - ECCV 2018, vol. 11211. Cham: Springer International Publishing; 2018. p. 3–19.

Chapter  Google Scholar 

Li H, Nan Y, Yang G. LKAU-Net: 3D Large-Kernel attention-based U-Net for automatic MRI brain tumor segmentation. In: Yang G, Aviles-Rivero A, Roberts M, Schönlieb CB, editors. Medical image understanding and analysis, vol. 13413. Cham: Springer International Publishing; 2022. p. 313–27.

Chapter  Google Scholar 

Belagiannis V, Bradley A, Cardoso JS, Carneiro G, Cornebise J, Loog M, et al, editors. Deep learning and data labeling for medical applications: First international workshop, LABELS 2016, and second international workshop, DLMIA 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings. 1st ed. No. 10008 in Image Processing, Computer Vision, Pattern Recognition, and Graphics. Cham: Springer International Publishing : Imprint: Springer; 2016.

Dou Q, Yu L, Chen H, Jin Y, Yang X, Qin J, et al. 3D deeply supervised network for automated segmentation of volumetric medical images. Med Image Anal. 2017;41:40–54. https://doi.org/10.1016/j.media.2017.05.001.

Roth HR, Oda H, Zhou X, Shimizu N, Yang Y, Hayashi Y, et al. An application of cascaded 3D fully convolutional networks for medical image segmentation. Comput Med Imaging Graph. 2018;66:90–9. https://doi.org/10.1016/j.compmedimag.2018.03.001.

Chen S, Roth H, Dorn S, May M, Cavallaro A, Lell MM, et al. Towards automatic abdominal multi-organ segmentation in dual energy CT using cascaded 3D fully convolutional network.

Kakeya H, Okada T, Oshiro Y. 3D U-JAPA-Net: Mixture of convolutional networks for abdominal multi-organ CT segmentation. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors. Medical image computing and computer assisted intervention - MICCAI 2018, vol. 11073. Cham: Springer International Publishing; 2018. p. 426–33.

Chapter  Google Scholar 

Zhou Y, Wang Y, Tang P, Bai S, Shen W, Fishman EK, et al. Semi-supervised multi-organ segmentation via deep multi-planar co-training.

Tang H, Liu X, Han K, Xie X, Chen X, Qian H, et al. Spatial context-aware self-attention model for multi-organ segmentation. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). Waikoloa, HI, USA: IEEE; 2021. p. 938–48.

Ma J, Zhang Y, Gu S, An X, Wang Z, Ge C, et al. Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge. Med Image Anal. 2022;82:102616.

Article  Google Scholar 

Zhang F, Wang Y. Efficient context-aware network for abdominal multi-organ segmentation. arXiv:2109.10601 [Preprint]. 2021. Available from: https://arxiv.org/abs/2109.10601.

Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, et al. UNETR: Transformers for 3D medical image segmentation. In: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Waikoloa, HI, USA: IEEE; 2022. p. 1748–58.

Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, et al. Advancing the cancer genome Atlas Glioma MRI collections with expert segmentation labels and radiomic features. Sci Data. 2017;4(1):170117. https://doi.org/10.1038/sdata.2017.117.

Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv:1811.02629 [Preprint]. 2019. Available from: http://arxiv.org/abs/1811.02629.

Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imag. 2015;34(10):1993–2024. https://doi.org/10.1109/TMI.2014.2377694.

Guan X, Yang G, Ye J, Yang W, Xu X, Jiang W, et al. 3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework. BMC Med Imag. 2022;22(1):6. https://doi.org/10.1186/s12880-021-00728-8.

Huang H, Yang G, Zhang W, Xu X, Yang W, Jiang W, et al. A deep multi-task learning framework for brain tumor segmentation. Front Oncol. 2021;11:690244. https://doi.org/10.3389/fonc.2021.690244.

Myronenko A. 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi A, Bakas S, Kuijf H, Keyvan F, Reyes M, van Walsum T, editors. Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries, vol. 11384. Cham: Springer International Publishing; 2019. p. 311–20.

Chapter  Google Scholar 

Isensee F, Jäger PF, Full PM, Vollmuth P, Maier-Hein KH. nnU-Net for brain tumor segmentation. In: Crimi A, Bakas S, editors. Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries, vol. 12659. Cham: Springer International Publishing; 2021. p. 118–32.

Chapter  Google Scholar 

Jiang Z, Ding C, Liu M, Tao D. Two-stage cascaded U-Net: 1st place solution to BraTS challenge 2019 segmentation task. In: Crimi A, Bakas S, editors. Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries, vol. 11992. Cham: Springer International Publishing; 2020. p. 231–41.

Chapter  Google Scholar 

Wang Y, Zhang Y, Hou F, Liu Y, Tian J, Zhong C, et al. Modality-pairing learning for brain tumor segmentation. In: Crimi A, Bakas S, editors., et al., Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries, vol. 12658. Cham: Springer International Publishing; 2021. p. 230–40.

Zhang W, Yang G, Huang H, Yang W, Xu X, Liu Y, et al. ME-Net: Multi-encoder net framework for brain tumor segmentation. Int J Imag Syst Technol. 2021;31(4):1834–48. https://doi.org/10.1002/ima.22571.

Futrega M, Milesi A, Marcinkiewicz M, Ribalta P. Optimized U-Net for brain tumor segmentation. In: BrainLes@MICCAI. 2022.

Luu HM, Park SH. Extending nn-UNet for brain tumor segmentation. In: BrainLes@MICCAI; 2022.

Zou K, Yuan X, Shen X, Wang M, Fu H. TBraTS: Trusted brain tumor segmentation.

Peng C, Zhang X, Yu G, Luo G, Sun J. Large kernel matters — improve semantic segmentation by global convolutional network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI: IEEE; 2017. p. 1743–51.

Ding X, Zhang X, Han J, Ding G. Scaling up your kernels to 31x31: Revisiting large kernel design in CNNs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2022. p. 11963–75.

Yang J, Hu T, Yang J, Zhang Z, Pan Y. Large kernel spatial pyramid pooling for semantic segmentation. In: Zhao Y, Barnes N, Chen B, Westermann R, Kong X, Lin C, editors. Image and graphics, vol. 11901. Cham: Springer International Publishing; 2019. p. 595–605.

Chapter  Google Scholar 

Feng H, Wang L, Li Y, Du A. LKASR: Large kernel attention for lightweight image super-resolution. Knowl Based Syst. 2022;252:109376. https://doi.org/10.1016/j.knosys.2022.109376.

Luo P, Xiao G, Gao X, Wu S. LKD-Net: Large kernel convolution network for single image dehazing.

Liu D, Zhang D, Song Y, Zhang F, O’Donnell LJ, Cai W. 3D large kernel anisotropic network for brain tumor segmentation. In: Cheng L, Leung ACS, Ozawa S, editors. Neural information processing, vol. 11307. Cham: Springer International Publishing; 2018. p. 444–54.

Chapter  Google Scholar 

Guo MH, Lu CZ, Liu ZN, Cheng MM, Hu SM. Visual attention network. arXiv:2202.09741 [Preprint]. 2022. Available from: http://arxiv.org/abs/2202.09741.

Hu J, Shen L, Albanie S, Sun G, Vedaldi A. Gather-Excite: Exploiting feature context in convolutional neural networks. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, editors. Advances in neural information processing systems. vol. 31. Curran Associates, Inc.; 2018.

Park J, Woo S, Lee JY, Kweon IS. BAM: Bottleneck attention module. arXiv:1807.06514 [Preprint]. 2018. Available from: http://arxiv.org/abs/1807.06514.

Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, et al. Residual attention network for image classification. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE; 2017. p. 6450–8.

Rister B, Yi D, Shivakumar K, Nobashi T, Rubin DL. CT-ORG, a new dataset for multiple organ segmentation in computed tomography. Sci Data. 2020;7(1):381. https://doi.org/10.1038/s41597-020-00715-8.

Bilic P, Christ PF, Vorontsov E, Chlebus G, Chen H, Dou Q, et al. The liver tumor segmentation benchmark (LiTS).

Yang G, Ye Q, Xia J. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review. Two Showcases Beyond Inf Fusion. 2022;77:29–52. https://doi.org/10.1016/j.inffus.2021.07.016.

Beheshti I, Ganaie M, Paliwal V, Rastogi A, Razzak I, Tanveer M. Predicting brain age using machine learning algorithms: a comprehensive evaluation. IEEE J Biomed Health Inform. 2021;26(4):1432–40.

Article  Google Scholar 

Tanveer M, Rashid AH, Ganaie M, Reza M, Razzak I, Hua KL. Classification of Alzheimer’s disease using ensemble of deep neural networks trained through transfer learning. IEEE J Biomed Health Inform. 2021;26(4):1453–63.

Article  Google Scholar 

Malik AK, Tanveer M. Graph embedded ensemble deep randomized network for diagnosis of Alzheimer’s disease. IEEE/ACM Trans Comput Biol Bioinform. 2022.

Nan Y, DelSer J, Walsh S, Schönlieb C, Roberts M, Selby I, et al. Data harmonisation for information fusion in digital healthcare: a state-of-the-art systematic review, meta-analysis and future research directions. Inf Fusion. 2022.

Xing X, DelSer J, Wu Y, Li Y, Xia J, Lei X, et al. HDL: Hybrid deep learning for the synthesis of myocardial velocity maps in digital twins for cardiac analysis. IEEE J Biomed Health Inform. 2022;1–1. https://doi.org/10.1109/JBHI.2022.3158897.

Xing X, Huang J, Nan Y, Wu Y, Wang C, Gao Z, et al. CS: a controllable and simultaneous synthesizer of images and annotations with minimal human intervention. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2022. p. 3–12.

留言 (0)

沒有登入
gif