scholarly journals Artificial Intelligence System Reduces False-Positive Findings in the Interpretation of Breast Ultrasound Exams

2021 ◽  
Author(s):  
Yiqiu Shen ◽  
Farah E. Shamout ◽  
Jamie R. Oliver ◽  
Jan Witowski ◽  
Kawshik Kannan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
False Positive ◽  
Imaging Modality ◽  
Characteristic Curve ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Intelligence System ◽  
External Test

AbstractUltrasound is an important imaging modality for the detection and characterization of breast cancer. Though consistently shown to detect mammographically occult cancers, especially in women with dense breasts, breast ultrasound has been noted to have high false-positive rates. In this work, we present an artificial intelligence (AI) system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. To develop and validate this system, we curated a dataset consisting of 288,767 ultrasound exams from 143,203 patients examined at NYU Langone Health, between 2012 and 2019. On a test set consisting of 44,755 exams, the AI system achieved an area under the receiver operating characteristic curve (AUROC) of 0.976. In a reader study, the AI system achieved a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924±0.02 radiologists). With the help of the AI, radiologists decreased their false positive rates by 37.4% and reduced the number of requested biopsies by 27.8%, while maintaining the same level of sensitivity. To confirm its generalizability, we evaluated our system on an independent external test dataset where it achieved an AUROC of 0.911. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis worldwide.

scholarly journals Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yiqiu Shen ◽  
Farah E. Shamout ◽  
Jamie R. Oliver ◽  
Jan Witowski ◽  
Kawshik Kannan ◽  
...  
Keyword(s):  
False Positive ◽  
Operating Characteristic ◽  
Color Doppler ◽  
Characteristic Curve ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Artificial Intelligence System ◽  
Test Set ◽  
Intelligence System ◽  
Operating Characteristic Curve

AbstractThough consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.

scholarly journals Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model

2021 ◽  
Vol 11 ◽  
Author(s):  
Xianyu Zhang ◽  
Hui Li ◽  
Chaoyun Wang ◽  
Wen Cheng ◽  
Yuntao Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Molecular Subtypes ◽  
Learning Model ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Test Set ◽  
External Test ◽  
Deep Learning Model

Background: Breast ultrasound is the first choice for breast tumor diagnosis in China, but the Breast Imaging Reporting and Data System (BI-RADS) categorization routinely used in the clinic often leads to unnecessary biopsy. Radiologists have no ability to predict molecular subtypes with important pathological information that can guide clinical treatment.Materials and Methods: This retrospective study collected breast ultrasound images from two hospitals and formed training, test and external test sets after strict selection, which included 2,822, 707, and 210 ultrasound images, respectively. An optimized deep learning model (DLM) was constructed with the training set, and the performance was verified in both the test set and the external test set. Diagnostic results were compared with the BI-RADS categorization determined by radiologists. We divided breast cancer into different molecular subtypes according to hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression. The ability to predict molecular subtypes using the DLM was confirmed in the test set.Results: In the test set, with pathological results as the gold standard, the accuracy, sensitivity and specificity were 85.6, 98.7, and 63.1%, respectively, according to the BI-RADS categorization. The same set achieved an accuracy, sensitivity, and specificity of 89.7, 91.3, and 86.9%, respectively, when using the DLM. For the test set, the area under the curve (AUC) was 0.96. For the external test set, the AUC was 0.90. The diagnostic accuracy was 92.86% with the DLM in BI-RADS 4a patients. Approximately 70.76% of the cases were judged as benign tumors. Unnecessary biopsy was theoretically reduced by 67.86%. However, the false negative rate was 10.4%. A good prediction effect was shown for the molecular subtypes of breast cancer with the DLM. The AUC were 0.864, 0.811, and 0.837 for the triple-negative subtype, HER2 (+) subtype and HR (+) subtype predictions, respectively.Conclusion: This study showed that the DLM was highly accurate in recognizing breast tumors from ultrasound images. Thus, the DLM can greatly reduce the incidence of unnecessary biopsy, especially for patients with BI-RADS 4a. In addition, the predictive ability of this model for molecular subtypes was satisfactory,which has specific clinical application value.

scholarly journals Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Cheng Jin ◽  
Weixiang Chen ◽  
Yukun Cao ◽  
Zhanwei Xu ◽  
Zimeng Tan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Influenza A ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Community Acquired Pneumonia ◽  
Intelligence System ◽  
Challenging Tasks ◽  
Chest X Ray ◽  
Detailed Interpretation ◽  
Timely Treatment

Abstract Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19.

scholarly journals Lesion Segmentation in Breast Ultrasound Images using Optimized Marked Watershed Method

2021 ◽  
Author(s):  
Xiaoyan Shen ◽  
He Ma ◽  
Ruibo Liu ◽  
Hong Li ◽  
Jiachuan He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Stage ◽  
Speckle Noise ◽  
Histogram Equalization ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Lesion Segmentation ◽  
Early Screening ◽  
Side Window

Abstract Background: Breast cancer is one of the most serious diseases threatening women’s health. Early screening based on ultrasound can help to detect and treat tumors in early stage. However, due to the lack of radiologists with professional skills, ultrasound based breast cancer screening has not been widely used in rural area. Computer-aided diagnosis (CAD) technology can effectively alleviates this problem. Since Breast Ultrasound (BUS) images have low resolution and speckle noise, lesion segmentation, which is an important step in CAD system, is challenging.Results: Two datasets were used for evaluation. Dataset A comprises 500 BUS images from local hospitals, while dataset B comprises 205 BUS images from open source. The experimental results show that the proposed method outperformed its related classic segmentation methods and the state-of-the-art deep learning model, RDAU–NET. And its’ Accuracy(Acc), Dice efficient(DSC) and Jaccard Index(JI) reached 96.25%, 78.4% and 65.34% on dataset A, and ACC, DC and Sen reached 97.96%, 86.25% and 88.79% on dataset B.Conclusions: We proposed an adaptive morphology snake based on marked watershed(AMSMW) algorithm for BUS images segmentation. It was proven to be robust, efficient and effective. In addition, it was found to be more sensitive to malignant lesions than benign lesions. What’s more, since the Rectangular Region of Interest(RROI) in the proposed method is drawn manually, we will consider adding deep learning network to automatically identify RROI, and completely liberate the hands of radiologists.Methods: The proposed method consists of two main steps. In the first step, we used Contrast Limited Adaptive Histogram Equalization(CLAHE) and Side Window Filter(SWF) to preprocess BUS images. Therefore, lesion contours can be effectively highlighted and the influence of noise can be eliminated to a great extent. In the second step, we proposed adaptative morphology snake(AMS) as an embedded segmentation function of AMSMW. It can adjust working parameters adaptively, according to different lesions’ size. By combining segmentation results of AMS with marker region obtained by morphological method, we got the marker region of marked watershed (MW). Finally, we obtained candidate contours by MW. And the best lesion contour was chosen by maximum Average Radial Derivative(ARD).

scholarly journals Differential diagnosis of breast cancer assisted by S-Detect artificial intelligence system

2021 ◽  
Vol 18 (4) ◽  
pp. 3680-3689
Author(s):  
Qun Xia ◽  
◽  
Yangmei Cheng ◽  
Jinhua Hu ◽  
Juxia Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Artificial Intelligence System ◽  
Intelligence System

scholarly journals Comparison of Three Segmentation Methods for Breast Ultrasound Images based on Level Set and Morphological Operations

2017 ◽  
Vol 7 (1) ◽  
pp. 383
Author(s):  
Dewi Putrie Lestari ◽  
Sarifuddin Madenda ◽  
Ernastuti Ernastuti ◽  
Eri Prasetyo Wibowo
Keyword(s):  
Breast Cancer ◽  
Level Set ◽  
Active Contour ◽  
Level Set Methods ◽  
Breast Ultrasound ◽  
Medical Image Segmentation ◽  
Image Processing Technique ◽  
Dice Similarity Coefficient ◽  
Ultrasound Images ◽  
Morphological Operations

Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (C-V) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), True-Negative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics.

Restoration and enhancement of breast ultrasound images using extended complex diffusion based unsharp masking

2021 ◽  
pp. 095441192110393 ◽  
Author(s):  
Abhinav Kumar ◽  
Subodh Srivastava
Keyword(s):  
Breast Cancer ◽  
Performance Evaluation ◽  
Quantitative Evaluation ◽  
Mean Squared Error ◽  
Imaging Modality ◽  
Ultrasound Images ◽  
Edge Preservation ◽  
Unsharp Masking ◽  
Complex Diffusion ◽  
Extended Complex

Ultrasound is a well-known imaging modality for the interpretation of breast cancer. It is playing very important role for breast cancer detection that are missed by mammograms. The image acquisition is usually affected by the presence of noise, artifacts, and distortion. To overcome such type of issues, there is a need of image restoration and enhancement to improve the quality of image. This paper proposes a single framework for denoising and enhancement of ultrasound images, where a smoothing filter is replaced with an extended complex diffusion-based filter in an unsharp masking technique. The performance evaluation of the proposed method is tested on real ultrasound breast cancer images database and synthetic ultrasound image. The performance evaluation comprises qualitative and quantitative evaluation along with comparative analysis of pre-existing and proposed method. The quantitative evaluation metrics are mean squared error, peak-signal-to-noise ratio, correlation parameter, normalized absolute error, universal quality index, similarity structure index, edge preservation index, a measure of enhancement, a measure of enhancement by entropy, and second derivative like measurement. The result specifies that the proposed method is better suited approach for the removal of speckle noise which follows Rayleigh distribution, restoration of information, enhancement of abnormalities, and proper edge preservation.

scholarly journals Classification of Breast Cancer Lesions in Ultrasound Images by Using Attention Layer and Loss Ensemble in Deep Convolutional Neural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1859
Author(s):  
Elham Yousef Kalafi ◽  
Ata Jodeiri ◽  
Seyed Kamaledin Setarehdan ◽  
Ng Wei Lin ◽  
Kartini Rahmat ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Loss Function ◽  
Low Cost ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Deep Convolutional Neural Networks ◽  
Hyperbolic Cosine ◽  
Reliable Classification ◽  
Network Approaches

The reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low-cost method for the early diagnosis of breast cancer. The accuracy of the diagnosis is, however, highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. In this study, we propose a new framework for the classification of breast cancer lesions with an attention module in a modified VGG16 architecture. The adopted attention mechanism enhances the feature discrimination between the background and targeted lesions in ultrasound. We also propose a new ensembled loss function, which is a combination of binary cross-entropy and the logarithm of the hyperbolic cosine loss, to improve the model discrepancy between classified lesions and their labels. This combined loss function optimizes the network more quickly. The proposed model outperformed other modified VGG16 architectures, with an accuracy of 93%, and also, the results are competitive with those of other state-of-the-art frameworks for the classification of breast cancer lesions. Our experimental results show that the choice of loss function is highly important and plays a key role in breast lesion classification tasks. Additionally, by adding an attention block, we could improve the performance of the model.

scholarly journals Perbandingan Teknik Reduksi Derau Speckle Pada Citra Ultrasonograpi Payudara

2018 ◽  
Vol 4 (2) ◽  
pp. 27-36
Author(s):  
Yuli Triyani
Keyword(s):  
Breast Cancer ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Reduction Techniques ◽  
Noise Filter ◽  
Incidence And Mortality ◽  
Structural Content ◽  
Aided Diagnosis

Breast cancer is the most commonly diagnosed cancer with the highest prevalence, incidence, and mortality rate for females in Indonesia and worldwide. Ultrasonography is a recommended modality for breast cancer, because it is comfortable, radiation free and it can be widely used. However, ultrasound images often occur in quality degradation caused by speckle noise that appears during image acquisition. It causes difficulty for radiologists or Computer Aided Diagnosis (CAD) systems to diagnose these images. Some techniques are proposed for reducing the speckle noise. This journal aims to compare the performance of 14 noise reduction techniques in breast ultrasound images. Quantitative testing was carried out on 58 breast ultrasound images and 3 artificial breast ultrasound image. The quantitative parameters are used include texture analysis (Mean, Variant, skewness, kurtosis, contrast and entropy) and evaluation of image quality (MSE, RMSE, SNR, SSIM, Structural content and Maximum Difference). The qualitative testing was also carried out with the assessment of 3 radiology specialists on 3 samples of each reduction technique. Based on test results, the 3 best performance filters are DsFsrad, DsFamedian dan DsFhmedian. Keywords: Ultrasound, speckle noise, filter

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