Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images

2020 ◽  
Vol 127 ◽  
pp. 108992 ◽  
Author(s):  
Hui Zhou ◽  
Yinhua Jin ◽  
Lei Dai ◽  
Meiwu Zhang ◽  
Yuqin Qiu ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Deep Learning ◽  
Thyroid Nodules ◽  
Ultrasound Images ◽  
Thyroid Ultrasound

scholarly journals Deep Learning Applications in Analyzing Ultrasound Images of Thyroid Nodules: Protocol for a Systematic Review

2020 ◽  
Vol 9 (1) ◽  
pp. 33
Author(s):  
Yasaman Sharifi ◽  
Saeed Eslami HassanAbady ◽  
Morteza Danai Ashgzari ◽  
Mahdi Sargolzaei
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Quality Assessment ◽  
English Language ◽  
Subjective Experience ◽  
Thyroid Nodules ◽  
Flow Diagram ◽  
Ultrasound Images ◽  
Thyroid Ultrasound ◽  
The Impact

Introduction: Ultrasound images are one of the main contributors for evaluating of thyroid nodules. However, reading ultrasound imaging is not easy and strongly depends to doctors’ experiences. Therefore, a CAD system could assist doctors in evaluating thyroid ultrasound images to reduce the impact of subjective experience on the diagnostic results. Objective: with the best of our knowledge there is not any articles that actually provide a systematic review of deep learning application in analyzing ultrasound images of thyroid nodules and Hence, a comprehensive review of studies in this field can be useful, therefore the protocol of this systematic Review will be presented to reach this goal.Method: This protocol includes five stages: research questions definition, search strategy design, study selection, quality assessment and data extraction. We developed search for relevant English language articles using the PubMed, Scopus and Science Direct. Inclusion and exclusion criteria were defined and flow diagram is conducted, from 623 studies retrieved, 27 studies were included, after quality assessment data was extracted based on defined categories.Result: The result of this systematic review can help researchers by comprehensive view and the summary of evidence to present new ideas and further research and represent a state of the art in this field.Conclusion: in this study a protocol was used for doing a systematic review on various deep learning applications in thyroid ultrasound such as feature selection, classification, localization, detection and segmentation. Articles were screened based on the following items: study and patient information, dataset, method, results and comparison method.

scholarly journals Deep Learning Based on ACR TI-RADS Can Improve the Differential Diagnosis of Thyroid Nodules

2021 ◽  
Vol 11 ◽  
Author(s):  
Ge-Ge Wu ◽  
Wen-Zhi Lv ◽  
Rui Yin ◽  
Jian-Wei Xu ◽  
Yu-Jing Yan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Ultrasound Images ◽  
Thyroid Ultrasound ◽  
Optimal Model ◽  
Test Set ◽  
Independent Test ◽  
Design And Methods ◽  
Benign Thyroid Nodules ◽  
Benign Thyroid

ObjectiveThe purpose of this study was to improve the differentiation between malignant and benign thyroid nodules using deep learning (DL) in category 4 and 5 based on the Thyroid Imaging Reporting and Data System (TI-RADS, TR) from the American College of Radiology (ACR).Design and MethodsFrom June 2, 2017 to April 23, 2019, 2082 thyroid ultrasound images from 1396 consecutive patients with confirmed pathology were retrospectively collected, of which 1289 nodules were category 4 (TR4) and 793 nodules were category 5 (TR5). Ninety percent of the B-mode ultrasound images were applied for training and validation, and the residual 10% and an independent external dataset for testing purpose by three different deep learning algorithms.ResultsIn the independent test set, the DL algorithm of best performance got an AUC of 0.904, 0.845, 0.829 in TR4, TR5, and TR4&5, respectively. The sensitivity and specificity of the optimal model was 0.829, 0.831 on TR4, 0.846, 0.778 on TR5, 0.790, 0.779 on TR4&5, versus the radiologists of 0.686 (P=0.108), 0.766 (P=0.101), 0.677 (P=0.211), 0.750 (P=0.128), and 0.680 (P=0.023), 0.761 (P=0.530), respectively.ConclusionsThe study demonstrated that DL could improve the differentiation of malignant from benign thyroid nodules and had significant potential for clinical application on TR4 and TR5.

A deep learning‐based method for detecting and classifying the ultrasound images of suspicious thyroid nodules

2021 ◽  
Author(s):  
Zijian Zhao ◽  
Congmin Yang ◽  
Qian Wang ◽  
Huawei Zhang ◽  
Linlin Shi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Ultrasound Images

Recognition of calcifications in thyroid nodules based on attention-gated collaborative supervision network of ultrasound images

2020 ◽  
Vol 28 (6) ◽  
pp. 1123-1139
Author(s):  
Liqun Zhang ◽  
Ke Chen ◽  
Lin Han ◽  
Yan Zhuang ◽  
Zhan Hua ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Thyroid Nodules ◽  
Ultrasound Images ◽  
Training Methods ◽  
Important Criterion ◽  
Improve Accuracy ◽  
Important Means ◽  
Model Training ◽  
Medical Dataset

BACKGROUND: Calcification is an important criterion for classification between benign and malignant thyroid nodules. Deep learning provides an important means for automatic calcification recognition, but it is tedious to annotate pixel-level labels for calcifications with various morphologies. OBJECTIVE: This study aims to improve accuracy of calcification recognition and prediction of its location, as well as to reduce the number of pixel-level labels in model training. METHODS: We proposed a collaborative supervision network based on attention gating (CS-AGnet), which was composed of two branches: a segmentation network and a classification network. The reorganized two-stage collaborative semi-supervised model was trained under the supervision of all image-level labels and few pixel-level labels. RESULTS: The results show that although our semi-supervised network used only 30% (289 cases) of pixel-level labels for training, the accuracy of calcification recognition reaches 92.1%, which is very close to 92.9% of deep supervision with 100% (966 cases) pixel-level labels. The CS-AGnet enables to focus the model’s attention on calcification objects. Thus, it achieves higher accuracy than other deep learning methods. CONCLUSIONS: Our collaborative semi-supervised model has a preferable performance in calcification recognition, and it reduces the number of manual annotations of pixel-level labels. Moreover, it may be of great reference for the object recognition of medical dataset with few labels.

scholarly journals Thyroid Nodules Classification in Medical Ultrasound Images using Deep Learning

2020 ◽  
Vol 9 (7) ◽  
pp. 1211-1215
Keyword(s):  
Deep Learning ◽  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Second Opinion ◽  
Learning Approach ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Cad System ◽  
Improve Accuracy

Ultrasound scanning is most excellent significant diagnosis techniques utilized for thyroid nodules identification. A thyroid nodule is unnecessary cells that can develop in your base of neck which can be normal or cancerous. Many Computer added diagnosis systems (CAD) have been developed as a second opinion for radiologist. The thyroid nodules classification using machine learning and deep learning approach is latest trend which is using to improve accuracy for differentiation of thyroid nodules from benign and malignant type. In this paper we review the most recent work on CAD system which uses different feature extraction technique and classifier used for thyroid nodules classification with deep learning approach. This paper we illustrate the result obtained by these studies and highlight the limitation of each proposed methods. Moreover we summarize convolution neural network (CNN) architecture for classification of thyroid nodule. This literature review is meant at researcher but it also useful for radiologist who is interesting in CAD tool in ultrasound imaging for second opinion.

Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images

2020 ◽  
Vol 47 (9) ◽  
pp. 3952-3960 ◽  
Author(s):  
Chao Sun ◽  
Yukang Zhang ◽  
Qing Chang ◽  
Tianjiao Liu ◽  
Shaohang Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Ultrasound Images ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Comparison Study of Radiomics and Deep Learning-Based Methods for Thyroid Nodules Classification Using Ultrasound Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 52010-52017 ◽  
Author(s):  
Yongfeng Wang ◽  
Wenwen Yue ◽  
Xiaolong Li ◽  
Shuyu Liu ◽  
Lehang Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Ultrasound Images ◽  
Comparison Study
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