Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses

Tsochatzidis, Lazaros; Κούτλα, Παναγιώτα; Costaridou, Lena; Pratikakis, Ioannis

doi:10.1016/j.cmpb.2020.105913

Cited by 75 publications

(40 citation statements)

References 19 publications

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“…The work was evaluated on six medical images datasets with multiple modalities, and it demonstrated consistent performance for semantic and instance segmentation tasks. A similar variation model, called U-Net+, was employed by Tsochatzidis et al 42 to segment ROI mass before integrating it with the classification stage by a CNN model. The segmentation performance showed a Dice score of 0.722 and 0.738, and a Jaccard index of 0.565 and 0.585, respectively, on the CBIS-DDSM and DDSM-400 datasets.…”

Section: Methodsmentioning

confidence: 99%

Connected-UNets: a deep learning architecture for breast mass segmentation

et al. 2021

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Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic systems for breast mass segmentation to assist radiologists in their diagnosis. With the rapid development of deep learning and its application to medical imaging challenges, UNet and its variations is one of the state-of-the-art models for medical image segmentation that showed promising performance on mammography. In this paper, we propose an architecture, called Connected-UNets, which connects two UNets using additional modified skip connections. We integrate Atrous Spatial Pyramid Pooling (ASPP) in the two standard UNets to emphasize the contextual information within the encoder–decoder network architecture. We also apply the proposed architecture on the Attention UNet (AUNet) and the Residual UNet (ResUNet). We evaluated the proposed architectures on two publically available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Experiments were also conducted using additional synthetic data using the cycle-consistent Generative Adversarial Network (CycleGAN) model between two unpaired datasets to augment and enhance the images. Qualitative and quantitative results show that the proposed architecture can achieve better automatic mass segmentation with a high Dice score of 89.52%, 95.28%, and 95.88% and Intersection over Union (IoU) score of 80.02%, 91.03%, and 92.27%, respectively, on CBIS-DDSM, INbreast, and the private dataset.

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Section: Methodsmentioning

confidence: 99%

Connected-UNets: a deep learning architecture for breast mass segmentation

et al. 2021

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“…Therefore, several works have incorporated the segmentation stage to provide a complete, signi cant diagnosis. In a previous work by Tsochatzidis et al [37] modi ed convolutional layers of a CNN to integrate both input images and their corresponding segmentation maps in order to improve the diagnosis of breast cancer. The method was applied on DDSM-400 and CBIS-DDSM datasets and achieved a diagnosis performance of AUC of 0.89 and 0.86.…”

Section: Technical Backgroundmentioning

confidence: 99%

“…This architecture accelerated the convergence of a larger number of deep layers, and consequently it has been found e cient to provide a compact representation of input images and improve the classi cation task performance [22]. The ResNet has some common architectures such as ResNet-50, 101, and 152, [37] which indicate the number of deep layers. Alternatively, ResNet architecture presented an improved version ResNetV2 by He et al [49], where the last ReLU was removed to clear the shortcut path using a simple identity connection as shown in Supplementary Figure .1.…”

Section: Model: Transfer Learning and Ne-tuningmentioning

confidence: 99%

An integrated Framework for Breast Mass Classification and Diagnosis using Stacked Ensemble of Residual Neural Networks

Baccouche

Garcia‐Zapirain

Elmaghraby

2022

Preprint

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A computer-aided diagnosis (CAD) system requires automated stages of tumor detection, segmentation, and classification that are integrated sequentially in one framework to assist the radiologists with a final diagnosis decision. In this paper, we introduce the final step of breast mass classification and diagnosis using a stacked ensemble of residual neural network (ResNet) models (i.e. ResNet50V2, ResNet101V2, and ResNet152V2). The work presents the task of classifying the detected and segmented breast masses into malignant or benign, and diagnosing the Breast Imaging Reporting and Data System (BI-RADS) assessment category with a score from 2 to 6 and the shape as oval, round, lobulated, or irregular. The proposed methodology was evaluated on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Comparative experiments were conducted on the individual models and an average ensemble of models with an XGBoost classifier. Qualitative and quantitative results show that the proposed model achieved better performance for 1) Pathology classification with an accuracy of 95.13%, 99.20%, and 95.88%; 2) BI-RADS category classification with an accuracy of 85.38%, 99%, and 96.08% respectively on CBIS-DDSM, INbreast, and the private dataset; and 3) shape classification with 90.02% on the CBIS-DDSM dataset. Our results demonstrate that our proposed integrated framework could benefit from all automated stages to outperform the latest deep learning methodologies.

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“…An experiment was performed by Tsochatzidis et al [17] to test the diagnosis of breast cancer with mammograms using CNN. ey show that performance assessment in diagnosis is carried out on two datasets of mammographic mass such as DDSM-400 and CBIS-DDSM, with variations in the accuracy of the corresponding segmentation maps of ground truth.…”

Section: Introductionmentioning

confidence: 99%

Boosting Breast Cancer Detection Using Convolutional Neural Network

Alanazi

Kamruzzaman

Sarker

et al. 2021

Journal of Healthcare Engineering

147

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Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs). The paper investigates the proposed system that uses various convolutional neural network (CNN) architectures to automatically detect breast cancer, comparing the results with those from machine learning (ML) algorithms. All architectures were guided by a big dataset of about 275,000, 50 × 50-pixel RGB image patches. Validation tests were done for quantitative results using the performance measures for every methodology. The proposed system is found to be successful, achieving results with 87% accuracy, which could reduce human mistakes in the diagnosis process. Moreover, our proposed system achieves accuracy higher than the 78% accuracy of machine learning (ML) algorithms. The proposed system therefore improves accuracy by 9% above results from machine learning (ML) algorithms.

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Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses

Cited by 75 publications

References 19 publications

Connected-UNets: a deep learning architecture for breast mass segmentation

Connected-UNets: a deep learning architecture for breast mass segmentation

An integrated Framework for Breast Mass Classification and Diagnosis using Stacked Ensemble of Residual Neural Networks

Boosting Breast Cancer Detection Using Convolutional Neural Network

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