A segmentation-based algorithm for classification of benign and malignancy Thyroid nodules with multi-feature information

Mallick UK. The Revised American Thyroid Association Management Guidelines 2009 for patients with differentiated thyroid cancer: an evidence based risk adapted approach. Clin Oncol. 2010;22(06):472–4.

Article  Google Scholar 

Véronique Terrasse. Global cancer burden growing, amidst mounting need for services. The International Agency for Research on Cancer (IARC), 1 February 2024, PRESS RELEASE No. 345.

Schlumberger M, Tahara M, Wirth LJ, Robinson B, Brose MS, Elisei R, Habra MA, Newbold K, Shah MH, Hoff AO, et al. Lenvatinib versus placebo in radioiodine-refractory Thyroid cancer. N Engl J Med. 2015;372(7):621–30.

Article  Google Scholar 

Yu X, Song X, Sun W, et al. Independent risk factors predicting central lymph node metastasis in papillary Thyroid microcarcinoma. Horm Metab Res. 2017;49(3):201–7.

Article  Google Scholar 

Wang J, Wei W, Guo R. Ultrasonic elastography and conventional ultrasound in the diagnosis of Thyroid micro-nodules. Pak J Med Sci. 2019;35(6):1526.

Article  Google Scholar 

Lian C, Liu M, Zhang J, et al. Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE Trans Pattern Anal Mach Intell. 2018;42(4):880–93.

Article  Google Scholar 

Zhang J, Zhang Z, Liu H, et al. SaTransformer: semantic-aware transformer for breast cancer classification and segmentation. IET Image Proc. 2023;17(13):3789–800.

Article  Google Scholar 

Ji Z, Zhao Z, Zeng X, et al. ResDSda_U-Net: A novel U-Net based residual network for segmentation of pulmonary nodules in lung CT images. IEEE Access. 2023;11:87775–87789.

Wang J, Zhang R, Wei X, et al. An attention-based semi-supervised neural network for Thyroid nodules segmentation. In: 2019 IEEE International conference on bioinformatics and biomedicine (BIBM). IEEE; 2019. pp. 871–876.

Ding J, Huang Z, Shi M, et al. Automatic Thyroid ultrasound image segmentation based on u-shaped network. In: 2019 12th International congress on image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE; 2019. pp. 1–5.

Nandamuri S, China D, Mitra P, et al. Sumnet: fully convolutional model for fast segmentation of anatomical structures in ultrasound volumes. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE; 2019. pp. 1729–1732.

Song R, Zhang L, Zhu C, et al. Thyroid nodule ultrasound image classification through hybrid feature cropping network. IEEE Access. 2020;8:64064–74.

Article  Google Scholar 

Nguyen DT, Pham TD, Batchuluun G, et al. Artificial intelligence-based Thyroid nodule classification using information from spatial and frequency domains. J Clin Med. 2019;8(11):1976.

Article  Google Scholar 

Misra S, Yoon C, Kim KJ, et al. Deep learning-based multimodal fusion network for segmentation and classification of breast cancers using B-mode and elastography ultrasound images. Bioeng Transl Med. 2022;8:e10480.

Article  Google Scholar 

Misra S, Jeon S, Managuli R, et al. Bi-modal transfer learning for classifying breast cancers via combined b-mode and ultrasound strain imaging. IEEE Trans Ultrason Ferroelectr Freq Control. 2021;69(1):222–32.

Article  Google Scholar 

Oktay O, Schlemper J, Folgoc LL, et al. Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999, 2018.

Wang P, Chen P, Yuan Y, et al. Understanding convolution for semantic segmentation. In: 2018 IEEE winter conference on applications of computer vision (WACV). IEEE; 2018. pp. 1451–1460.

Chen LC, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017.

Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18. Springer International Publishing; 2015. pp. 234–241.

Chen LC, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV); 2018. pp. 801–818.

He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. pp. 770–778.

Ho Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2021. pp. 13713–13722.

Tang Y, Yang D, Li W, et al. Self-supervised pre-training of swin transformers for 3d medical image analysis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022. pp. 20730–20740.

Isensee F, Petersen J, Klein A, et al. nnu-net: Self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486, 2018.

Gao Y, Zhou M, Liu D, et al. A data-scalable transformer for medical image segmentation: architecture, model efficiency, and benchmark. arXiv preprint arXiv:2203.00131, 2022.

Gong H, Chen J, Chen G, et al. Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules. Comput Biol Med. 2023;155:106389.

Article  Google Scholar 

Pedraza L, Vargas C, Narváez F, et al. An open access thyroid ultrasound image database. In: 10th International symposium on medical information processing and analysis. SPIE, 2015, 9287. pp. 188–193.

Wunderling T, Golla B, Poudel P, et al. Comparison of thyroid segmentation techniques for 3D ultrasound. In: Medical imaging 2017: image processing. SPIE, 2017;10133:346–352.

Gong H, Chen G, Wang R, et al. Multi-task learning for thyroid nodule segmentation with thyroid region prior. In: 2021 IEEE 18th international symposium on biomedical imaging (ISBI). IEEE; 2021. pp. 257–261.

Feng S, Zhao H, Shi F, et al. CPFNet: Context pyramid fusion network for medical image segmentation. IEEE Trans Med Imaging. 2020;39(10):3008–18.

Article  Google Scholar 

Wang S, Li Z, Liao L, et al. DPAM-PSPNet: ultrasonic image segmentation of thyroid nodule based on dual-path attention mechanism. Phys Med Biol. 2023;68(16): 165002.

Article  Google Scholar 

Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. pp. 4700–4708.

Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16×16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.

Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. pp. 2818–2826.

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.

Zhang Y, Lai H, Yang W. Cascade UNet and CH-UNet for Thyroid nodule segmentation and benign and malignancy classification. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham; 2020. pp. 129–134.

Comments (0)

No login
gif