Convolutional neural networks for breast cancer detection in mammography: A survey

Abdelrahman, Leila; Ghamdi, Manal Al; Collado-Mesa, Fernando; Abdel-Mottaleb, Mohamed

doi:10.1016/j.compbiomed.2021.104248

Cited by 93 publications

(48 citation statements)

References 71 publications

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“…Detecting and classifying masses in mammograms using deep learning is widely covered in the literature (Abdelrahman et al, 2021). A large variety of deep learning models have been developed to assist radiologists in screening mammography.…”

Section: Mass Detection In Ffdm Using Deep Learningmentioning

confidence: 99%

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Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study

Garrucho¹,

Kushibar²,

Jouide³

et al. 2022

Preprint

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Computer-aided detection systems based on deep learning have shown a great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this work, we explore the domain generalization of deep learning methods for mass detection in digital mammography and analyze in-depth the sources of domain shift in a large-scale multi-center setting. To this end, we compare the performance of eight state-of-the-art detection methods, including Transformer-based models, trained in a single-domain and tested in five unseen domains. Moreover, a single-source mass detection training pipeline is designed to improve the domain generalization without requiring images from the new domain. The results show that our workflow generalizes better than state-of-the-art transfer learning-based approaches in four out of five domains, while reducing the domain shift caused by the different acquisition protocols and scanner manufacturers. Subsequently, an extensive analysis is performed to identify the covariate shifts with bigger effects on the detection performance, such as due to differences in patient age, breast density, mass size and mass malignancy. Ultimately, this comprehensive study provides key insights and best practices for future research on domain generalization in deep learning-based breast cancer detection.

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Section: Mass Detection In Ffdm Using Deep Learningmentioning

confidence: 99%

“…In breast cancer mass detection, the True Positive Rate (TPR), also known as sensitivity or recall, is commonly used as the metric of reference to evaluate the performance of the CADe systems (Abdelrahman et al, 2021). The TPR penalizes the missed masses and rewards the detected ones.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study

Garrucho¹,

Kushibar²,

Jouide³

et al. 2022

Preprint

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“…Therefore, a great amount of computer-aided detection (CAD) systems have been developed to improve the efficiency of mammogram interpretation. Recently, deep learning models have been used to measure the likelihood of cancer from a mammogram [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ], some of which were designed to detect and classify micro-calcifications or calcified lesions [ 8 , 9 , 10 ], mass lesions [ 11 , 12 , 13 , 14 ] and even all the contained lesions [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Breast lesions found in mammograms are mainly classified into normal, benign and malignant [ 15 , 16 , 17 , 18 , 19 , 20 ] and are further classified as normal, benign calcification, benign mass, malignant calcification and malignant mass [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography

Tsai

Chou

et al. 2022

Sensors

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Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.

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“…Screening mammography (MMG) provides early detection and contributes to reducing breast cancer mortality [1].…”

Section: Introductionmentioning

confidence: 99%

Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification

Szafranowska,

Osuala,

Breier

et al. 2022

Preprint

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Early detection of breast cancer in mammography screening via deep-learning based computer-aided detection systems shows promising potential in improving the curability and mortality rates of breast cancer. However, many clinical centres are restricted in the amount and heterogeneity of available data to train such models to (i) achieve promising performance and to (ii) generalise well across acquisition protocols and domains. As sharing data between centres is restricted due to patient privacy concerns, we propose a potential solution: sharing trained generative models between centres as substitute for real patient data. In this work, we use three well known mammography datasets to simulate three different centres, where one centre receives the trained generator of Generative Adversarial Networks (GANs) from the two remaining centres in order to augment the size and heterogeneity of its training dataset. We evaluate the utility of this approach on mammography patch classification on the test set of the GAN-receiving centre using two different classification models, (a) a convolutional neural network and (b) a transformer neural network. Our experiments demonstrate that shared GANs notably increase the performance of both transformer and convolutional classification models and highlight this approach as a viable alternative to inter-centre data sharing.

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Convolutional neural networks for breast cancer detection in mammography: A survey

Cited by 93 publications

References 71 publications

Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study

Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study

A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography

Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification

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