A novel wavelet-transform-based convolution classification network for cervical lymph node metastasis of papillary thyroid carcinoma in ultrasound images
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Chengfan Li | Ren Wang | Xuehai Ding | Chentian Shen | Quanyong Luo | Junjuan Zhao | Yanting Liu | Ren Wang | Chengfan Li
[1] T. Vogl,et al. Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer , 2022, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.
[2] Peiying Wei,et al. DeepThy‐Net: A Multimodal Deep Learning Method for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Cancer , 2022, Adv. Intell. Syst..
[3] Wanlei Zhu,et al. Artificial Neural Network-Based Ultrasound Radiomics Can Predict Large-Volume Lymph Node Metastasis in Clinical N0 Papillary Thyroid Carcinoma Patients , 2022, Journal of oncology.
[4] D. Shen,et al. GAN-Guided Deformable Attention Network for Identifying Thyroid Nodules in Ultrasound Images , 2022, IEEE Journal of Biomedical and Health Informatics.
[5] Shawn H. Sun,et al. Deep learning prediction of axillary lymph node status using ultrasound images , 2022, Comput. Biol. Medicine.
[6] M. Sahu,et al. Improving the classification performance of breast ultrasound image using deep learning and optimization algorithm , 2021, 2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES).
[7] Yong Chen,et al. Predictions for Central Lymph Node Metastasis of Papillary Thyroid Carcinoma via CNN-Based Fusion Modeling of Ultrasound Images , 2021, Traitement du Signal.
[8] T. Kuwert,et al. Advanced thyroid carcinomas: neural network analysis of ultrasonographic characteristics , 2021, Thyroid Research.
[9] A. Jemal,et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries , 2021, CA: a cancer journal for clinicians.
[10] Lun Zhang,et al. A multiple-channel and atrous convolution network for ultrasound image segmentation. , 2020, Medical physics.
[11] Yi Guo,et al. Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics , 2020, Nature Communications.
[12] Hui-Xiong Xu,et al. A Comparative Analysis of Two Machine Learning-based Diagnostic Patterns with ACR TI-RADS for Thyroid Nodules: Diagnostic Performance and Unnecessary Biopsy Rate. , 2020, Thyroid : official journal of the American Thyroid Association.
[13] Jie Tian,et al. Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images. , 2020, European Journal of Radiology.
[14] Jie Yang,et al. Thyroid Nodule Ultrasound Image Classification Through Hybrid Feature Cropping Network , 2020, IEEE Access.
[15] Ramzi Guetari,et al. Thyroid nodules classification and diagnosis in ultrasound images using fine‐tuning deep convolutional neural network , 2020, Int. J. Imaging Syst. Technol..
[16] Ruey-Feng Chang,et al. Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks , 2020, Comput. Methods Programs Biomed..
[17] J. Youk,et al. Radiomics signature for prediction of lateral lymph node metastasis in conventional papillary thyroid carcinoma , 2020, PloS one.
[18] Chunfeng Lian,et al. Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks , 2019, Medical Image Anal..
[19] X. Cui,et al. Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning. , 2019, Radiology.
[20] Kang Ryoung Park,et al. Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains , 2019, Journal of clinical medicine.
[21] Mohammad I. Daoud,et al. Automatic superpixel-based segmentation method for breast ultrasound images , 2019, Expert Syst. Appl..
[22] Dong Ni,et al. Deep Learning in Medical Ultrasound Analysis: A Review , 2019, Engineering.
[23] P. Jacob,et al. Visualizing and evaluating wetting in membrane distillation: New methodology and indicators based on Detection of Dissolved Tracer Intrusion (DDTI) , 2018, Desalination.
[24] Jeong Hyun Lee,et al. Deep Learning-Based Computer-Aided Diagnosis System for Localization and Diagnosis of Metastatic Lymph Nodes on Ultrasound: A Pilot Study. , 2018, Thyroid : official journal of the American Thyroid Association.
[25] D. Dong,et al. Radiomic signature as a predictive factor for lymph node metastasis in early‐stage cervical cancer , 2018, Journal of magnetic resonance imaging : JMRI.
[26] E. J. Ha,et al. Computer-Aided Diagnosis of Thyroid Nodules via Ultrasonography: Initial Clinical Experience , 2018, Korean journal of radiology.
[27] 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.
[28] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[29] S. Mandel,et al. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. , 2009, Thyroid : official journal of the American Thyroid Association.
[30] J. Shah,et al. Impact of lymph node metastasis in differentiated carcinoma of the thyroid: A matched‐pair analysis , 1996, Head & neck.
[31] Henry Tabe,et al. Wavelet Transform , 2009, Encyclopedia of Biometrics.