Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network

Fujioka, Tomoyuki; Kubota, Kazunori; Mori, M.; Kikuchi, Yuka; Katsuta, Leona; Kasahara, Mai; Oda, Goshi; Ishiba, Toshiyuki; Nakagawa, Tsuyoshi; Tateishi, Ukihide

doi:10.1007/s11604-019-00831-5

Cited by 139 publications

(88 citation statements)

References 20 publications

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“…COVID-19, coronavirus disease 2019; CT, computed tomography; RT-PCR, reverse transcription-polymerase chain reaction; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2. suspected of having COVID-19 pneumonia, it is crucial to make a comprehensive assessment based on both CT images and clinical findings. In recent years, artificial intelligence, especially deep learning, has been greatly developed and applied to medical imaging [16][17][18][19]. Ni et al utilized a deep learning model for automatic detection of abnormalities in chest CT images of COVID-19 patients.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the Usefulness of CO-RADS for Chest CT in Patients Suspected of Having COVID-19

et al. 2020

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The purpose of this study was to use the Coronavirus Disease 2019 (COVID-19) Reporting and Data System (CO-RADS) to evaluate the chest computed tomography (CT) images of patients suspected of having COVID-19, and to investigate its diagnostic performance and interobserver agreement. The Dutch Radiological Society developed CO-RADS as a diagnostic indicator for assessing suspicion of lung involvement of COVID-19 on a scale of 1 (very low) to 5 (very high). We investigated retrospectively 154 adult patients with clinically suspected COVID-19, between April and June 2020, who underwent chest CT and reverse transcription-polymerase chain reaction (RT-PCR). The patients’ average age was 61.3 years (range, 21–93), 101 were male, and 76 were RT-PCR positive. Using CO-RADS, four radiologists evaluated the chest CT images. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Interobserver agreement was calculated using the intraclass correlation coefficient (ICC) by comparing the individual reader’s score to the median of the remaining three radiologists. The average sensitivity was 87.8% (range, 80.2–93.4%), specificity was 66.4% (range, 51.3–84.5%), and AUC was 0.859 (range, 0.847–0.881); there was no significant difference between the readers (p > 0.200). In 325 (52.8%) of 616 observations, there was absolute agreement among observers. The average ICC of readers was 0.840 (range, 0.800–0.874; p < 0.001). CO-RADS is a categorical taxonomic evaluation scheme for COVID-19 pneumonia, using chest CT images, that provides outstanding performance and from substantial to almost perfect interobserver agreement for predicting COVID-19.

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Section: Discussionmentioning

confidence: 99%

Evaluation of the Usefulness of CO-RADS for Chest CT in Patients Suspected of Having COVID-19

et al. 2020

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“…In recent years, artificial intelligence (AI), especially the deep learning (DL) method with convolutional neural networks (CNNs), has accomplished outstanding performances in medical breast imaging for pattern recognition, object detection, segmentation, and image synthesis [8][9][10][11][12]. Object detection-detecting instances of semantic objects of a certain class in digital images-is one of the most important computer technologies related to image processing.…”

Section: Introductionmentioning

confidence: 99%

Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

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We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

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“…Cross Validation [27] Melanoma Histology Rotation Estimation [28] Lung cancer Histology Not mentioned [29] Lung conditions Expert consent Resampling Process [30] Dermatological cancer Histology Resampling Process [31] Breast tumor Histology Not mentioned [32] Glaucoma…”

Section: Expert Consentmentioning

confidence: 99%

A Comparison of Transfer Learning Performance Versus Health Experts in Disease Diagnosis From Medical Imaging

et al. 2020

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Deep learning methods have huge success in task specific feature representation. Transfer learning algorithms are very much effective when large training data is scarce. It has been significantly used for diagnosis of diseases in medical imaging. This article presents a systematic literature review (SLR) by conducting a comparison of a variety of transfer learning approaches with healthcare experts in diagnosing diseases from medical imaging. This study has been compiled by reviewing research studies published in renowned venues between 2014 and 2019. Moreover, the data for the diagnosis performed by health care experts has also been acquired to perform a detailed comparative analysis for a wide range of diseases. The analysis has been performed on the basis of diseases, transfer learning approaches, type of medical imaging used. The comparative analysis is based on performance indices reported in studies which include diagnostic accuracy, true-positive (TP), false-positive (FP), true-negative (TN), false-negative (FN) sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). A total of5,188articles were identified out of which 63 studies were included. Among them 21 research studies contain sufficient data to construct the evaluation tables that enable process of test accuracy of transfer learning having sensitivity ranged from 71% to 100% (mean 85.25%) and specificity ranged from 64% to 100% (mean 81.92%). Furthermore, health experts having sensitivity ranged from 33% to 100% (mean 85.27%) and specificity ranged from 82% to 100% (mean 91.63%).This SLR found that diagnostic accuracy of transfer learning is approximately equivalent to the diagnosis of health experts. The results also revealed that convolutional neural networks (CNN) have been extensively used for disease diagnosis from medical imaging. Finally, inappropriate exposure of diseases in transfer learning studies restricts reliable elucidation of the outcomes of diagnostic accuracy.

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Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network

Cited by 139 publications

References 20 publications

Evaluation of the Usefulness of CO-RADS for Chest CT in Patients Suspected of Having COVID-19

Evaluation of the Usefulness of CO-RADS for Chest CT in Patients Suspected of Having COVID-19

Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

A Comparison of Transfer Learning Performance Versus Health Experts in Disease Diagnosis From Medical Imaging

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