2019
DOI: 10.1016/j.ultras.2018.07.006
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Medical breast ultrasound image segmentation by machine learning

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Cited by 213 publications
(104 citation statements)
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“…Due to limited medical datasets, it is usually more efficient to use transfer learning and adjust a pretrained deep model to address the classification problem of interest. Transfer learning methods were employed for breast mass classification and segmentation in several studies . Additionally, deep learning was used to detect breast lesions and differentiate breast masses with shear‐wave elastography .…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Due to limited medical datasets, it is usually more efficient to use transfer learning and adjust a pretrained deep model to address the classification problem of interest. Transfer learning methods were employed for breast mass classification and segmentation in several studies . Additionally, deep learning was used to detect breast lesions and differentiate breast masses with shear‐wave elastography .…”
Section: Introductionmentioning
confidence: 99%
“…Transfer learning methods were employed for breast mass classification and segmentation in several studies. [19][20][21][22][23][24][25] Additionally, deep learning was used to detect breast lesions 26 and differentiate breast masses with shear-wave elastography. 27 The betterperforming pretrained deep learning models have been developed using RGB color images.…”
Section: Introductionmentioning
confidence: 99%
“…To date, most examples of clinically useful DL image interpretation algorithms have focused on radiology‐based implementations such as interpretations of chest radiography, computed tomography (CT), and magnetic resonance imaging (MRI) . Ultrasound (US) examples of DL use for image analysis are relatively few and far between, with most being found in high‐end consultative imaging–type US machines …”
mentioning
confidence: 99%
“…Machine learning as the science of “artificial intelligence” (i.e., how computers learn from data), is rapidly evolving to include healthcare applications that could revolutionize medical imaging and diagnostics globally. Machine learning models for health are often based on convolutional neural networks (CNNs); the type of deep learning algorithms commonly applied to image classification and segmentation . Applications directly relevant to women's reproductive health include: reproductive medicine; obstetric imaging; breast imaging; and cervical cancer screening …”
Section: Machine Learning In Women's Healthmentioning
confidence: 99%