Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches

Amit, Guy; Ben-Ari, Rami; Hadad, Omer; Monovich, Einat; Granot, Noa; Hashoul, Sharbell Y.

doi:10.1117/12.2249981

Cited by 38 publications

(31 citation statements)

References 10 publications

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“…We discuss the brain-tumor data augmentation techniques already available in the literature, and divide them into several groups depending on their underlying concepts (section 2). Such MRI data augmentation approaches have been applied to augment other datasets as well, also acquired for different organs (Amit et al, 2017;Nguyen et al, 2019;Oksuz et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

259

127

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Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a very common problem in medical image analysis, especially tumor delineation. In this paper, we review the current advances in data-augmentation techniques applied to magnetic resonance images of brain tumors. To better understand the practical aspects of such algorithms, we investigate the papers submitted to the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018 edition), as the BraTS dataset became a standard benchmark for validating existent and emerging brain-tumor detection and segmentation techniques. We verify which data augmentation approaches were exploited and what was their impact on the abilities of underlying supervised learners. Finally, we highlight the most promising research directions to follow in order to synthesize high-quality artificial brain-tumor examples which can boost the generalization abilities of deep models.

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Section: Introductionmentioning

confidence: 99%

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

259

127

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“…Compared with traditional methods, the main advantage of deep learning models is its capability in extracting highly representative features in a data‐driven way. Recently, the efficacy of deep neural networks has been evaluated in breast cancer classification tasks . However, these works either used a small‐size dataset or needed manual annotations on lesions during the training phase.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, the efficacy of deep neural networks has been evaluated in breast cancer classification tasks. [28][29][30] However, these works either used a small-size dataset or needed manual annotations on lesions during the training phase. Directly localizing breast cancers in 3D radiology images with only image-level supervision has not yet been extensively explored.…”

Section: Discussionmentioning

confidence: 99%

Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images

Zhou

Luo

Dou

et al. 2019

Magnetic Resonance Imaging

119

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Background The usefulness of 3D deep learning‐based classification of breast cancer and malignancy localization from MRI has been reported. This work can potentially be very useful in the clinical domain and aid radiologists in breast cancer diagnosis. Purpose To evaluate the efficacy of 3D deep convolutional neural network (CNN) for diagnosing breast cancer and localizing the lesions at dynamic contrast enhanced (DCE) MRI data in a weakly supervised manner. Study Type Retrospective study. Subjects A total of 1537 female study cases (mean age 47.5 years ±11.8) were collected from March 2013 to December 2016. All the cases had labels of the pathology results as well as BI‐RADS categories assessed by radiologists. Field Strength/Sequence 1.5 T dynamic contrast‐enhanced MRI. Assessment Deep 3D densely connected networks were trained under image‐level supervision to automatically classify the images and localize the lesions. The dataset was randomly divided into training (1073), validation (157), and testing (307) subsets. Statistical Tests Accuracy, sensitivity, specificity, area under receiver operating characteristic curve (ROC), and the McNemar test for breast cancer classification. Dice similarity for breast cancer localization. Results The final algorithm performance for breast cancer diagnosis showed 83.7% (257 out of 307) accuracy (95% confidence interval [CI]: 79.1%, 87.4%), 90.8% (187 out of 206) sensitivity (95% CI: 80.6%, 94.1%), 69.3% (70 out of 101) specificity (95% CI: 59.7%, 77.5%), with the area under the curve ROC of 0.859. The weakly supervised cancer detection showed an overall Dice distance of 0.501 ± 0.274. Data Conclusion 3D CNNs demonstrated high accuracy for diagnosing breast cancer. The weakly supervised learning method showed promise for localizing lesions in volumetric radiology images with only image‐level labels. Level of Evidence: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1144–1151.

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“…However, the ROIs in their training dataset were only extracted from the DCE-MRI slices at the second post-contrast time point, which ignored kinetic and 3D context features. Amit et al [21] proposed a multichannel representation for DCE-MRI images that could capture both the anatomical and metabolic characteristics of lesions in a single multi-channel image and enabled a high accuracy. However, their dataset was relatively small, and they could not assess the classification in a single patient.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning-Based DCE-MRI Method for Identifying Differentiation Between Benign and Malignant Breast Tumors

et al. 2020

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In this paper, we propose a transfer learning-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) method for classifying fibroadenoma and invasive ductal carcinoma (IDC) in breast tumors. A total of 207 breast tumors from patients were collected and identified by pathologic diagnosis within 15 days after enhanced DCE-MRI examination; 119 patients (average age 50.52±10.33 years) had fibroadenomas, and 88 patients (average age 42.20±10.10 years) had IDCs. Two lesion-level models were built based on the InceptionV3 and VGG19 models, which were pretrained with the ImageNet dataset. The effects of different depths of transfer learning were examined. The network's performance was evaluated through five-fold cross validation. In the lesion-level models, the model based on Inception V3 obtained better results (area under the receiver operating characteristic curve (AUC) = 0.89) when the weights were frozen before layer-276. The other model based on VGG19 obtained better results (AUC = 0.87) when the weights were frozen before layer-13. Compared with the image-level models, both lesion-level models displayed better discrimination (accuracy increased by 13% and 14%) based on the VGG19 and Inception V3 models, respectively. Our research confirms that transfer learning can be utilized to classify fibroadenomas and IDCs in DCE-MRI images. Different depths of transfer learning result in different performances, and our proposed lesion-level model notably improves the classification accuracy. INDEX TERMS Magnetic resonance imaging, invasive ductal carcinoma, fibroadenoma, transfer learning, lesion classification.

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Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches

Cited by 38 publications

References 10 publications

Data Augmentation for Brain-Tumor Segmentation: A Review

Data Augmentation for Brain-Tumor Segmentation: A Review

Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images

Transfer Learning-Based DCE-MRI Method for Identifying Differentiation Between Benign and Malignant Breast Tumors

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