scholarly journals Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound

Head & Neck ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 885-891 ◽  
Author(s):  
Su Yeon Ko ◽  
Ji Hye Lee ◽  
Jung Hyun Yoon ◽  
Hyesun Na ◽  
Eunhye Hong ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Thyroid Nodules ◽  
Deep Convolutional Neural Network

scholarly journals Using the Deep Convolutional Neural Network to Evaluate Thyroid Nodules With Atypia of Undetermined Significance/follicular Lesion of Undetermined Significance Cytology: Multicenter Study

2021 ◽  
Author(s):  
Inyoung Youn ◽  
Eunjung Lee ◽  
Jung Hyun Yoon ◽  
Hye Sun Lee ◽  
Mi-Ri Kwon ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Interobserver Variability ◽  
Needle Aspiration ◽  
Deep Convolutional Neural Network ◽  
Fine Needle ◽  
Experience Levels ◽  
Undetermined Significance

Abstract To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS) results on fine-needle aspiration (FNA). This study included 202 patients with 202 nodules ≥ 1cm AUS/FLUS on FNA, and underwent surgery in one of 3 different institutions. Diagnostic performances were compared between 8 physicians (4 radiologists, 4 endocrinologists) with varying experience levels and CNN, and AUS/FLUS subgroups were analyzed. Interobserver variability was assessed among the 8 physicians. Of the 202 nodules, 158 were AUS, and 44 were FLUS; 86 were benign, and 116 were malignant. The area under the curves (AUCs) of the 8 physicians and CNN were 0.680-0.722 and 0.666, without significant differences (P > 0.05). In the subgroup analysis, the AUCs for the 8 physicians and CNN were 0.657–0.768 and 0.652 for AUS, 0.469-0.674 and 0.622 for FLUS. Interobserver agreements were moderate (k=0.543), substantial (k=0.652), and moderate (k=0.455) among the 8 physicians, 4 radiologists, and 4 endocrinologists. For thyroid nodules with AUS/FLUS cytology, the diagnostic performance of CNN to differentiate malignancy with US images was comparable to that of physicians with variable experience levels.

scholarly journals Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Ying Ren ◽  
Yu He ◽  
Linghua Cong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Loss Rate ◽  
Thyroid Nodules ◽  
Test Sample ◽  
Deep Convolutional Neural Network ◽  
Accuracy Rate ◽  
Average Accuracy ◽  
Training Samples ◽  
Average Loss

Objective. To investigate the application value of a deep convolutional neural network (CNN) model for cytological assessment of thyroid nodules. Methods. 117 patients with thyroid nodules who underwent thyroid cytology examination in the Affiliated People’s Hospital of Ningbo University between January 2017 and December 2019 were included in this study. 100 papillary thyroid cancer samples and 100 nonmalignant samples were collected respectively. The sample images were translated vertically and horizontally. Thus, 900 images were separately created in the vertical and horizontal directions. The sample images were randomly divided into training samples (n = 1260) and test samples (n = 540) at the ratio of 7 : 3 per the training sample to test sample. According to the training samples, the pretrained deep convolutional neural network architecture Resnet50 was trained and fine-tuned. A convolutional neural network-based computer-aided detection (CNN-CAD) system was constructed to perform full-length scan of the test sample slices. The ability of CNN-CAD to screen malignant tumors was analyzed using the threshold setting method. Eighty pathological images were collected from patients who received treatment between January 2020 and May 2020 and used to verify the value of CNN in the screening of malignant thyroid nodules as verification set. Results. With the number of iterations increasing, the training and verification loss of CNN model gradually decreased and tended to be stable, and the training and verification accuracy of CNN model gradually increased and tended to be stable. The average loss rate of training samples determined by the CNN model was (22.35 ± 0.62) %, and the average loss rate of test samples determined by the CNN model was (26.41 ± 3.37) %. The average accuracy rate of training samples determined by the CNN model was (91.04 ± 2.11) %, and the average accuracy rate of test samples determined by the CNN model was (91.26 ± 1.02)%. Conclusion. A CNN model exhibits a high value in the cytological diagnosis of thyroid diseases which can be used for the cytological diagnosis of malignant thyroid tumor in the clinic.

scholarly journals Diagnosing thyroid nodules with atypia of undetermined significance/follicular lesion of undetermined significance cytology with the deep convolutional neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Inyoung Youn ◽  
Eunjung Lee ◽  
Jung Hyun Yoon ◽  
Hye Sun Lee ◽  
Mi-Ri Kwon ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Interobserver Variability ◽  
Needle Aspiration ◽  
Deep Convolutional Neural Network ◽  
Fine Needle ◽  
Experience Levels ◽  
Undetermined Significance

AbstractTo compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS) results on fine-needle aspiration (FNA). This study included 202 patients with 202 nodules ≥ 1 cm AUS/FLUS on FNA, and underwent surgery in one of 3 different institutions. Diagnostic performances were compared between 8 physicians (4 radiologists, 4 endocrinologists) with varying experience levels and CNN, and AUS/FLUS subgroups were analyzed. Interobserver variability was assessed among the 8 physicians. Of the 202 nodules, 158 were AUS, and 44 were FLUS; 86 were benign, and 116 were malignant. The area under the curves (AUCs) of the 8 physicians and CNN were 0.680–0.722 and 0.666, without significant differences (P > 0.05). In the subgroup analysis, the AUCs for the 8 physicians and CNN were 0.657–0.768 and 0.652 for AUS, 0.469–0.674 and 0.622 for FLUS. Interobserver agreements were moderate (k = 0.543), substantial (k = 0.652), and moderate (k = 0.455) among the 8 physicians, 4 radiologists, and 4 endocrinologists. For thyroid nodules with AUS/FLUS cytology, the diagnostic performance of CNN to differentiate malignancy with US images was comparable to that of physicians with variable experience levels.

scholarly journals Diagnosis of thyroid nodules on ultrasonography by a deep convolutional neural network

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jieun Koh ◽  
Eunjung Lee ◽  
Kyunghwa Han ◽  
Eun-Kyung Kim ◽  
Eun Ju Son ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Thyroid Nodules ◽  
Medical Center ◽  
Deep Convolutional Neural Network ◽  
University Hospital ◽  
Test Set ◽  
Severance Hospital ◽  
Test Sets ◽  
External Test

Abstract The purpose of this study was to evaluate and compare the diagnostic performances of the deep convolutional neural network (CNN) and expert radiologists for differentiating thyroid nodules on ultrasonography (US), and to validate the results in multicenter data sets. This multicenter retrospective study collected 15,375 US images of thyroid nodules for algorithm development (n = 13,560, Severance Hospital, SH training set), the internal test (n = 634, SH test set), and the external test (n = 781, Samsung Medical Center, SMC set; n = 200, CHA Bundang Medical Center, CBMC set; n = 200, Kyung Hee University Hospital, KUH set). Two individual CNNs and two classification ensembles (CNNE1 and CNNE2) were tested to differentiate malignant and benign thyroid nodules. CNNs demonstrated high area under the curves (AUCs) to diagnose malignant thyroid nodules (0.898–0.937 for the internal test set and 0.821–0.885 for the external test sets). AUC was significantly higher for CNNE2 than radiologists in the SH test set (0.932 vs. 0.840, P < 0.001). AUC was not significantly different between CNNE2 and radiologists in the external test sets (P = 0.113, 0.126, and 0.690). CNN showed diagnostic performances comparable to expert radiologists for differentiating thyroid nodules on US in both the internal and external test sets.

scholarly journals Comparison of Diagnostic Performance in Thyroid Nodules on US: Deep Convolutional Neural Network Models vs Endocrinologists With Various Experiences

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A859-A859
Author(s):  
Sunyoung Kang ◽  
Eunjung Lee ◽  
Yoo Hyung Kim ◽  
Seul Ki Kwon ◽  
Kyong Yeun Jung ◽  
...  
Keyword(s):  
Neural Network ◽  
Thyroid Cancer ◽  
Convolutional Neural Network ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Deep Convolutional Neural Network ◽  
University Hospital ◽  
Neural Network Models ◽  
Maximal Diameter ◽  
Lower Accuracy

Abstract Objectives: To diagnose thyroid cancer, ultrasonography is a primary tool, but diagnostic accuracy varies according to the proficiency of clinicians. The aim of this study was to compare diagnostic performance between deep convolutional neural network (CNN) models and endocrinologist with various experiences. Methods: Patients who underwent fine needle aspiration at endocrinology department in Seoul National University Hospital, between April 2014 and June 2019, were reviewed. Among them, thyroid nodules which were pathologically confirmed by surgery and maximal diameter greater than 1cm were included. Ultrasonography images of thyroid nodules were reviewed by 13 endocrinologists with various experiences: 0 month (E0, n=8), 1 year (E1, n=2), and &gt;5 years (E5, n=3). Results: Of total 451 thyroid nodules, 66.5% was diagnosed as cancer and 83.7% was papillary thyroid cancer (PTC). Sensitivity and specificity of CNN were 85.3% and 63.6%, respectively, and its area under the curve (AUC) was 0.855. Compared to CNN, mean accuracy of E0 group was significantly lower (Accuracy 68.7% vs 78.0%, P &lt;0.001), and after CNN-assistance, that of E0 was significantly improved (68.7% [before] vs 73.93% [after], P = 0.008). E1 and E5 groups showed similar diagnostic performance to CNN, and CNN-assistance did not change it. Next, subgroup analysis was performed according to the histologic subtypes. AUC of CNN in PTC (0.925) was much higher than that of other cancers including FTC (0.529). Interestingly, CNN-assistance significantly improved diagnostic performance for PTC not only in beginners (E0), but also a subset of experienced endocrinologist (E1 and E5). Conclusions: CNN has good diagnostic performance in the diagnosis of PTC. Endocrinologist with lower experience in ultrasonography, CNN-assistance is beneficial for improving diagnostic performance especially in PTC.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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