Improved Differential Diagnosis of Breast Masses on Ultrasonographic Images with a Computer-Aided Diagnosis Scheme for Determining Histological Classifications

Kashikura, Yumi; Nakayama, Ryohei; Hizukuri, Akiyoshi; Noro, Aya; Nohara, Yuki; Nakamura, Takashi; Ito, Minori; Kimura, Hiroko; Yamashita, M; Hanamura, Noriko; Ogawa, Tomoko

doi:10.1016/j.acra.2012.11.007

Cited by 12 publications

(4 citation statements)

References 12 publications

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“…A multiple-discriminant analysis with the nine features was employed to distinguish among the four different types of histological classifications. We also confirmed the clinicians’ classification performances were substantially improved by using our CADx scheme in an observer study [ 7 ]. However, the unsatisfactory classification accuracy of our CADx scheme made it unfit to apply in clinical practice.…”

Section: Introductionsupporting

confidence: 70%

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

Hizukuri

Nakayama

2018

Diagnostics

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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.

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Section: Introductionsupporting

confidence: 70%

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

Hizukuri

Nakayama

2018

Diagnostics

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“…Several studies found that radiologists’ classification performance of lung nodules improved when they were assisted that provided a malignancy likelihood [20,21]. Similar observations were also reported for breast mass classification [22,23]. This shows that estimating the uncertainty of the automated model can be useful; however, it has been often overlooked when designing deep neural networks (DNN) models for health-care.…”

Section: Related Workmentioning

confidence: 63%

Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis

Mobiny

Singh

Nguyen

2019

JCM

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Knowing when a machine learning system is not confident about its prediction is crucial in medical domains where safety is critical. Ideally, a machine learning algorithm should make a prediction only when it is highly certain about its competency, and refer the case to physicians otherwise. In this paper, we investigate how Bayesian deep learning can improve the performance of the machine–physician team in the skin lesion classification task. We used the publicly available HAM10000 dataset, which includes samples from seven common skin lesion categories: Melanoma (MEL), Melanocytic Nevi (NV), Basal Cell Carcinoma (BCC), Actinic Keratoses and Intraepithelial Carcinoma (AKIEC), Benign Keratosis (BKL), Dermatofibroma (DF), and Vascular (VASC) lesions. Our experimental results show that Bayesian deep networks can boost the diagnostic performance of the standard DenseNet-169 model from 81.35% to 83.59% without incurring additional parameters or heavy computation. More importantly, a hybrid physician–machine workflow reaches a classification accuracy of 90 % while only referring 35 % of the cases to physicians. The findings are expected to generalize to other medical diagnosis applications. We believe that the availability of risk-aware machine learning methods will enable a wider adoption of machine learning technology in clinical settings.

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“…The ability of CAD to improve the radiologists' performances was based on providing a useful reference for radiologists either by consistently extracting the quantitative features or by uniformly estimating the malignancy probability. Some studies 20,21 have suggested that the role of CAD might be to serve as an alternative to double reading by radiologists. In clinical practice, variability in the second human reader exists.…”

Section: Discussionmentioning

confidence: 99%

Application of Computer-Aided Diagnosis to the Sonographic Evaluation of Cervical Lymph Nodes

Zhang

Wang

et al. 2015

Ultrason Imaging

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We initiated an observer study to evaluate a computerized system developed in our previous study for automatic extraction of 10 features and estimation of the malignancy probability of cervical nodes in sonograms. In the present study, five expert radiologists and five resident radiologists interpreted the sonograms of 178 nodes. The malignancy rating and patient management recommendation (biopsy or follow-up) were made without and then with the computer aid. Under these two reading conditions, the performances of radiologists and agreement among a group of radiologists were evaluated by using the receiver operating characteristic (ROC) analysis and the κ statistic, respectively. With the computer aid, the performances of radiologists improved significantly, as indicated by the increase in the area under the ROC curve (Az) from 0.843 to 0.896 (p = 0.031) and from 0.705 to 0.822 (p < 0.001), for the expert and resident groups, respectively. Agreement among all 10 radiologists improved from slight to moderate as indicated by an increase in the κ value from 0.195 to 0.421 (p < 0.001). The average performance of residents with aid (Az = 0.822) was close to that of experts without aid (Az = 0.843). Results indicate that computer-aided diagnosis is useful to improve radiologist performance (especially that of inexperienced radiologists) in the ultrasonographic evaluation of cervical nodes and to reduce variability among radiologists.

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Improved Differential Diagnosis of Breast Masses on Ultrasonographic Images with a Computer-Aided Diagnosis Scheme for Determining Histological Classifications

Cited by 12 publications

References 12 publications

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

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

Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis

Application of Computer-Aided Diagnosis to the Sonographic Evaluation of Cervical Lymph Nodes

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