New convolutional neural network model for screening and diagnosis of mammograms

Zhang, Chen; Zhao, Jumin; Niu, Jing; Li, Dengao

doi:10.1371/journal.pone.0237674

Cited by 30 publications

(20 citation statements)

References 40 publications

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“…Content may change prior to final publication. [11], [22]- [24]. Our results were consistent with those studies.…”

Section: G Comparison To Related Worksupporting

confidence: 93%

“…Compared with other related work [22]- [24], our model gives more localized class activation maps, especially in small calcification regions. This might be a benefit of a strong multi-scale feature representation of our network.…”

Section: Class Activation Maps For Breast Calcification Localizationmentioning

confidence: 80%

“…The multi-scale architecture was previously investigated for the diagnosis of mammography images by Zhang et al [24]. Their study was performed on a public scanned-film mammography database and incorporated only the blockwise multi-scale capability into DenseNet without the layerwise multi-scale DLA structure.…”

Section: G Comparison To Related Workmentioning

confidence: 99%

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Multi-Scale Convolutional Neural Networks for Classification of Digital Mammograms With Breast Calcifications

2021

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Breast cancer is the most commonly diagnosed cancer in women. Mammography is a widely used tool for breast cancer screening. Breast calcifications appear on mammograms as tiny white spots or grainy dots with a variety of clustering structures which may be time-consuming and difficult to identify by the human eye. Recent advances in multi-scale architectures have demonstrated strong feature representation for both large-scale contextual information and small-scale features. We hypothesized that the new architectures could be employed for classification of mammograms with breast calcifications and possibly for localization of breast calcification regions. In this study, we investigated different multi-scale architectures on 1,617 mammograms that contained only breast calcifications locally curated from our institution. Our best multi-scale attention network with hierarchical block-wise and layerwise feature representation capability achieved an accuracy of 84.34% and an area under the receiver operating characteristic curve (AUROC) of 90.36%. Similar performance was observed across breast density categories. Our network simultaneously processes both craniocaudal (CC) and mediolateral oblique (MLO) views of the breast providing class activation maps that were able to precisely localize breast calcification regions in a weakly supervised manner, indicating the benefits of strong feature representation through the multi-scale architectures.

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“…Content may change prior to final publication. [11], [22]- [24]. Our results were consistent with those studies.…”

Section: G Comparison To Related Worksupporting

confidence: 93%

Section: Class Activation Maps For Breast Calcification Localizationmentioning

confidence: 80%

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Multi-Scale Convolutional Neural Networks for Classification of Digital Mammograms With Breast Calcifications

2021

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“…Zhang et al [11] created a multiview feature fusion network method for classifying mammograms from 2 perceptions, and also they presented a multiscale attention DenseNet as the support network for FE method. The method includes 2 independent branches, i.e., utilized for extracting the features of 2 mammograms from distinct perceptions.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Jos�Escorcia-Gutierrez¹,

Mansour²,

Bele駉³

et al. 2022

Computers, Materials &Amp; Continua

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Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.

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“…There is a trend toward algorithms processing multiple mammography views of the same patient simultaneously (34)(35)(36)(37)(38). In clinical practice, two views of a breast, called Craniocaudal (CC) and Medio-lateral Oblique (MLO), are usually acquired from two different angles.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Multi-Task Learning to Cope With Poor and Missing Labels of Mammograms

Tardy¹,

Mateus²

2022

Front. Radio

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In breast cancer screening, binary classification of mammograms is a common task aiming to determine whether a case is malignant or benign. A Computer-Aided Diagnosis (CADx) system based on a trainable classifier requires clean data and labels coming from a confirmed diagnosis. Unfortunately, such labels are not easy to obtain in clinical practice, since the histopathological reports of biopsy may not be available alongside mammograms, while normal cases may not have an explicit follow-up confirmation. Such ambiguities result either in reducing the number of samples eligible for training or in a label uncertainty that may decrease the performances. In this work, we maximize the number of samples for training relying on multi-task learning. We design a deep-neural-network-based classifier yielding multiple outputs in one forward pass. The predicted classes include binary malignancy, cancer probability estimation, breast density, and image laterality. Since few samples have all classes available and confirmed, we propose to introduce the uncertainty related to the classes as a per-sample weight during training. Such weighting prevents updating the network's parameters when training on uncertain or missing labels. We evaluate our approach on the public INBreast and private datasets, showing statistically significant improvements compared to baseline and independent state-of-the-art approaches. Moreover, we use mammograms from Susan G. Komen Tissue Bank for fine-tuning, further demonstrating the ability to improve the performances in our multi-task learning setup from raw clinical data. We achieved the binary classification performance of AUC = 80.46 on our private dataset and AUC = 85.23 on the INBreast dataset.

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New convolutional neural network model for screening and diagnosis of mammograms

Cited by 30 publications

References 40 publications

Multi-Scale Convolutional Neural Networks for Classification of Digital Mammograms With Breast Calcifications

Multi-Scale Convolutional Neural Networks for Classification of Digital Mammograms With Breast Calcifications

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Leveraging Multi-Task Learning to Cope With Poor and Missing Labels of Mammograms

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