scholarly journals Computer-Aided Diagnosis of Breast Lesions

2002 ◽  
Author(s):  
Yulei Jiang
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Lesions ◽  
Computer Aided ◽  
Aided Diagnosis

Clinical Application of Computer-Aided Diagnosis According to S-Detect in Category 4 Breast Lesions: Prospective Single-Center Study (Preprint)

2020 ◽  
Author(s):  
Pengfei Sun ◽  
Chen Chen ◽  
Weiqi Wang ◽  
Lei Liang ◽  
Dan Luo ◽  
...  
Keyword(s):  
Clinical Management ◽  
Diagnostic Performance ◽  
Breast Biopsy ◽  
Computer Aided Diagnosis ◽  
Breast Lesions ◽  
Breast Masses ◽  
Computer Aided ◽  
Conventional Ultrasound ◽  
Us Findings ◽  
Aided Diagnosis

BACKGROUND Computer-aided diagnosis (CAD) is a useful tool that can provide a reference for the differential diagnosis of benign and malignant breast lesion. Previous studies have demonstrated that CAD can improve the diagnostic performance. However, conventional ultrasound (US) combined with CAD were used to adjust the classification of category 4 lesions has been few assessed. OBJECTIVE The objective of our study was to evaluate the diagnosis performance of conventional ultrasound combined with a CAD system S-Detect in the category of BI-RADS 4 breast lesions. METHODS Between December 2018 and May 2020, we enrolled patients in this study who received conventional ultrasound and S-Detect before US-guided biopsy or surgical excision. The diagnostic performance was compared between US findings only and the combined use of US findings with S-Detect, which were correlated with pathology results. RESULTS A total of 98 patients (mean age 51.06 ±16.25 years, range 22-81) with 110 breast masses (mean size1.97±1.38cm, range0.6-8.5) were included in this study. Of the 110 breast masses, 64/110 (58.18%) were benign, 46/110 (41.82%) were malignant. Compared with conventional ultrasound, a significant increase in specificity (0% to 53.12%, P<.001), accuracy (41.81% to70.19%, P<.001) were noted, with no statistically significant decrease on sensitivity(100% to 95.65% ,P=.48). According to S-Detect-guided US BI-RADS re-classification, 30 out of 110 (27.27%) breast lesions underwent a correct change in clinical management, 74of 110 (67.27%) breast lesions underwent no change and 6 of 110 (5.45%) breast lesions underwent an incorrect change in clinical management. The biopsy rate decreased from 100% to 67.27 % (P<.001).Benign masses among subcategory 4a had higher rates of possibly benign assessment on S-Detect for the US only (60% to 0%, P<.001). CONCLUSIONS S-Detect can be used as an additional diagnostic tool to improve the specificity and accuracy in clinical practice. S-Detect have the potential to be used in downgrading benign masses misclassified as BI-RADS category 4 on US by radiologist, and may reduce unnecessary breast biopsy. CLINICALTRIAL none

Computer-aided diagnosis in breast DCE-MRI—Quantification of the heterogeneity of breast lesions

2012 ◽  
Vol 81 (7) ◽  
pp. 1532-1538 ◽  
Author(s):  
Uta Preim ◽  
Sylvia Glaßer ◽  
Bernhard Preim ◽  
Frank Fischbach ◽  
Jens Ricke
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Lesions ◽  
Dce Mri ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Potential of computer-aided diagnosis of high spectral and spatial resolution (HiSS) MRI in the classification of breast lesions

2013 ◽  
Vol 39 (1) ◽  
pp. 59-67 ◽  
Author(s):  
Neha Bhooshan ◽  
Maryellen Giger ◽  
Milica Medved ◽  
Hui Li ◽  
Abbie Wood ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Computer Aided Diagnosis ◽  
Breast Lesions ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Breast Lesions on Sonograms: Computer-aided Diagnosis with Nearly Setting-Independent Features and Artificial Neural Networks

Radiology ◽  
2003 ◽  
Vol 226 (2) ◽  
pp. 504-514 ◽  
Author(s):  
Chung-Ming Chen ◽  
Yi-Hong Chou ◽  
Ko-Chung Han ◽  
Guo-Shian Hung ◽  
Chui-Mei Tiu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computer Aided Diagnosis ◽  
Breast Lesions ◽  
Computer Aided ◽  
Artificial Neural ◽  
Independent Features ◽  
Aided Diagnosis

scholarly journals Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network

Diagnostics ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 48 ◽  
Author(s):  
Akiyoshi Hizukuri ◽  
Ryohei Nakayama
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Area Under The Curve ◽  
Computer Aided Diagnosis ◽  
Previous Method ◽  
Breast Lesions ◽  
Computer Aided ◽  
Invasive Carcinomas ◽  
Fully Connected ◽  
Aided Diagnosis

It can be difficult for clinicians to accurately discriminate among histological classifications of breast lesions on ultrasonographic images. The purpose of this study was to develop a computer-aided diagnosis (CADx) scheme for determining histological classifications of breast lesions using a convolutional neural network (CNN). Our database consisted of 578 breast ultrasonographic images. It included 287 malignant (217 invasive carcinomas and 70 noninvasive carcinomas) and 291 benign lesions (111 cysts and 180 fibroadenomas). In this study, the CNN constructed from four convolutional layers, three batch-normalization layers, four pooling layers, and two fully connected layers was employed for distinguishing between the four different types of histological classifications for lesions. The classification accuracies for histological classifications with our CNN model were 83.9–87.6%, which were substantially higher than those with our previous method (55.7–79.3%) using hand-crafted features and a classifier. The area under the curve with our CNN model was 0.976, whereas that with our previous method was 0.939 (p = 0.0001). Our CNN model would be useful in differential diagnoses of breast lesions as a diagnostic aid.

Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis

2015 ◽  
Vol 314 ◽  
pp. 293-310 ◽  
Author(s):  
Qinghua Huang ◽  
Feibin Yang ◽  
Longzhong Liu ◽  
Xuelong Li
Keyword(s):  
Automatic Segmentation ◽  
Computer Aided Diagnosis ◽  
Breast Lesions ◽  
Computer Aided ◽  
Aided Diagnosis
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