Deep learning radiomics for non-invasive diagnosis of benign and malignant thyroid nodules using ultrasound images

2020 ◽  
Author(s):  
Hui Zhou ◽  
Kun Wang ◽  
Jie Tian
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Ultrasound Images ◽  
Non Invasive

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

Detection of Thyroid Nodules with Ultrasound Images Based on Deep Learning

2020 ◽  
Vol 16 (2) ◽  
pp. 174-180 ◽  
Author(s):  
Xia Yu ◽  
Hongjie Wang ◽  
Liyong Ma
Keyword(s):  
Deep Learning ◽  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Detection Method ◽  
Ultrasound Images ◽  
True Negative ◽  
Automated Method ◽  
High Incidence ◽  
Sensitivity Specificity ◽  
Physician Workload

Background: Thyroid nodules are a common clinical entity with high incidence. Ultrasound is often employed to detect and evaluate thyroid nodules. The development of an efficient automated method to detect thyroid nodules using ultrasound has the potential to reduce both physician workload and operator-dependence. Objective: To study the method of automatic detection of thyroid nodules based on deep learning using ultrasound, and to obtain the detection method with higher accuracy and better performance. Methods: A total of 1200 ultrasound images of thyroid nodules and 800 ultrasound thyroid images without nodule are collected. An improved faster R-CNN based detection method of thyroid nodule is proposed. Instead of using VGG16 as the backbone, ResNet is employed as the backbone for faster R-CNN. SVM, CNN and Faster-RCNN methods are used for thyroid nodule detection test. Precision, sensitivity, specificity and F1-score indicators are used to evaluate the detection performance of different methods. Results: The method based on deep learning is superior to that based on SVM. Faster R-CNN method and the improved method are better than CNN method. Compared with VGG16 as the backbone, RestNet101 backbone based faster R-CNN method achieves better thyroid detection effect. From the accuracy index, the proposed method is 0.084, 0.032 and 0.019 higher than SVM, CNN and faster R-CNN, respectively. Similar results can be seen in precision, sensitivity, specificity and F1-Score indicators. Conclusion: The proposed method of deep learning achieves the best performance values with the highest true positive and true negative detection compared to other methods and performs best in the detection of thyroid nodules.

scholarly journals Efficient Deep Learning Architecture for Detection and Recognition of Thyroid Nodules

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jingzhe Ma ◽  
Shaobo Duan ◽  
Ye Zhang ◽  
Jing Wang ◽  
Zongmin Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Data Augmentation ◽  
Experimental Results ◽  
Local Health ◽  
Ultrasound Images ◽  
Detection Time ◽  
Average Precision ◽  
Medical Image Diagnosis ◽  
Detection And Recognition

Ultrasonography is widely used in the clinical diagnosis of thyroid nodules. Ultrasound images of thyroid nodules have different appearances, interior features, and blurred borders that are difficult for a physician to diagnose into malignant or benign types merely through visual recognition. The development of artificial intelligence, especially deep learning, has led to great advances in the field of medical image diagnosis. However, there are some challenges to achieve precision and efficiency in the recognition of thyroid nodules. In this work, we propose a deep learning architecture, you only look once v3 dense multireceptive fields convolutional neural network (YOLOv3-DMRF), based on YOLOv3. It comprises a DMRF-CNN and multiscale detection layers. In DMRF-CNN, we integrate dilated convolution with different dilation rates to continue passing the edge and the texture features to deeper layers. Two different scale detection layers are deployed to recognize the different sizes of the thyroid nodules. We used two datasets to train and evaluate the YOLOv3-DMRF during the experiments. One dataset includes 699 original ultrasound images of thyroid nodules collected from a local health physical center. We obtained 10,485 images after data augmentation. Another dataset is an open-access dataset that includes ultrasound images of 111 malignant and 41 benign thyroid nodules. Average precision (AP) and mean average precision (mAP) are used as the metrics for quantitative and qualitative evaluations. We compared the proposed YOLOv3-DMRF with some state-of-the-art deep learning networks. The experimental results show that YOLOv3-DMRF outperforms others on mAP and detection time on both the datasets. Specifically, the values of mAP and detection time were 90.05 and 95.23% and 3.7 and 2.2 s, respectively, on the two test datasets. Experimental results demonstrate that the proposed YOLOv3-DMRF is efficient for detection and recognition of thyroid nodules for ultrasound images.

scholarly journals Diagnosis of Significant Liver Fibrosis in Patients With Chronic Hepatitis B By Using a Deep Learning-Based Data Integration Network

2021 ◽  
Author(s):  
Zhong Liu ◽  
Huiying Wen ◽  
Ziqi Zhu ◽  
Qinyuan Li ◽  
Li Liu ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Deep Learning ◽  
Liver Fibrosis ◽  
Liver Stiffness ◽  
Liver Parenchyma ◽  
Ultrasound Images ◽  
Clinical Parameters ◽  
Non Invasive ◽  
Deep Learning Model ◽  
Significant Liver Fibrosis

Abstract Background and aims: Chronic hepatitis B virus (CHB) infection remains a major global health burden and the non-invasive and accurate diagnosis of significant liver fibrosis (≥F2) in CHB patients is clinically very important. This study aimed to assess the potential of the joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients’ clinical parameters in a deep learning model to improve the diagnosis of ≥F2 in CHB patients. Methods Of 527 CHB patients who underwent US examination, liver elastography and biopsy, 284 eligible patients were included. We developed a deep learning-based data integration network (DI-Net) to fuse the information of ultrasound images of liver parenchyma, liver stiffness values and patients’ clinical parameters for diagnosing ≥F2 in CHB patients. The performance of DI-Net was cross-validated in a main cohort (n =155) of the included patients and externally validated in an independent cohort (n = 129), with comparisons against single-source data-based models and other non-invasive methods in terms of the area under the receiver-operating-characteristic curve (AUC). Results DI-Net achieved an AUC of 0.943 (95% confidence interval [CI]: 0.893-0.973) in the cross-validation, and an AUC of 0.901 (95% CI: 0.834-0.945) in the external validation, which were significantly greater than those of the comparative methods (AUC ranges: 0.774-0.877 and 0.741-0.848 for cross- and external validations, respectively, Ps < 0.01). Conclusion The joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients’ clinical parameters in a deep learning model could significantly improve the diagnosis of ≥F2 in CHB patients.

Thyroid nodules risk stratification through deep learning based on ultrasound images

2020 ◽  
Author(s):  
Ziyu Bai ◽  
Luchen Chang ◽  
Ruiguo Yu ◽  
Xuewei Li ◽  
Xi Wei ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Thyroid Nodules ◽  
Ultrasound Images
Sign in / Sign up

Export Citation Format

Share Document