A Comparison Between a Deep Convolutional Neural Network and Radiologists for Classifying Regions of Interest in Mammography

Kooi, Thijs; Gubern-Mérida, Albert; Mordang, Jan-Jurre; Mann, Ritse M.; Pijnappel, Ruud M; Schuur, Klaas H.; Heeten, Ard den; Karssemeijer, Nico

doi:10.1007/978-3-319-41546-8_7

Cited by 32 publications

(24 citation statements)

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“…Solitary cyst and malignant mass patches from a diagnostic dataset are subsequently presented to the network and features are extracted from the hidden layers and used for the diagnosis task. The network employed is similar to the one in Ref …”

Section: Methodsmentioning

confidence: 99%

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Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network

et al. 2017

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We have presented a computer-aided diagnosis (CADx) method to discriminate cysts from solid lesion in mammography using features from a deep CNN trained on a large set of mass candidates, obtaining an AUC of 0.80 on a set of diagnostic exams recalled from screening. We believe the system shows great potential and comes close to the performance of recently developed spectral mammography. We think the system can be further improved when more data and computational power becomes available.

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Section: Methodsmentioning

confidence: 99%

“…The CNN was implemented in Theano . We used a VGG‐like network architecture that was similar to the one employed in Kooi et al . with two additional convolutional layers.…”

Section: Methodsmentioning

confidence: 99%

Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network

et al. 2017

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“…[18][19][20][21][22] Advances in GPUs, availability of large labeled data sets, and corresponding novel optimization methods have led to the development of CNNs with deeper architecture. The DCNNs have recently shown success in various medical image analysis tasks such as segmentation, detection, and classification in mammography, 23 urinary bladder, 24 thoracic-abdominal lymph nodes and interstitial lung disease, 11,25 and pulmonary perifissural nodules. 26 Because of the local connectivity, shared weights, and local pooling properties of DCNNs, feature learning, i.e., feature extraction and selection, is inherently embedded into the training process.…”

Section: Introductionmentioning

confidence: 99%

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

et al. 2016

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Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. Methods: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Results: Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). Conclusions: The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data s...

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“…is will minimize the variance between the training and validation sets and any future testing sets. Data augmentation has been used in many studies along with DL and CNN such as [192][193][194][195][196].…”

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confidence: 99%

Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

Ramadan

2020

Journal of Healthcare Engineering

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According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.

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A Comparison Between a Deep Convolutional Neural Network and Radiologists for Classifying Regions of Interest in Mammography

Cited by 32 publications

References 8 publications

Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network

Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

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