New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence

Gao, Yiming; Geras, Krzysztof J.; Lewin, Alan A.; Moy, Linda

doi:10.2214/ajr.18.20392

Cited by 89 publications

(59 citation statements)

References 64 publications

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“…As a result, our model has the ability to learn both local features across the entire image as well as macroscopic features such as symmetry between breasts. For a more comprehensive review of prior work, refer to one of the recent reviews [ 33 ], [ 34 ].…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening

Phang

Park

et al. 2020

IEEE Trans. Med. Imaging

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464

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We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network achieves an AUC of 0.895 in predicting whether there is a cancer in the breast, when tested on the screening population. We attribute the high accuracy of our model to a two-stage training procedure, which allows us to use a very high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and find our model to be as accurate as experienced radiologists when presented with the same data. Finally, we show that a hybrid model, averaging probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To better understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, model design, training procedure, errors, and properties of its internal representations.deep learning | deep convolutional neural networks | breast cancer screening | mammography B reast cancer is the second leading cancer-related cause of death among women in the US. In 2014, over 39 million screening and diagnostic mammography exams were performed in the US. It is estimated that in 2015 232,000 women were diagnosed with breast cancer and approximately 40,000 died from it (1). Although mammography is the only imaging test that has reduced breast cancer mortality (2-4), there has been discussion regarding the potential harms of screening, including false positive recalls and associated false positive biopsies. The vast majority of the 10-15% of women asked to return following an inconclusive screening mammogram undergo another mammogram and/or ultrasound for clarification. After the additional imaging exams, many of these findings are determined as benign and only 10-20% are recommended to undergo a needle biopsy for further work-up. Among these, only 20-40% yield a diagnosis of cancer (5). Evidently, there is an unmet need to shift the balance of routine breast cancer screening towards more benefit and less harm.Traditional computer-aided detection (CAD) in mammography is routinely used by radiologists to assist with image interpretation, despite multicenter studies showing these CAD programs do not improve their diagnostic performance (6).These CAD programs typically use handcrafted features to mark sites on a mammogram that appear distinct from normal tissue structures. The radiologist decides whether to recall these findings, determining clinical significance and actionability. Recent developments in deep learning (7)-in particular, deep convolutional neural networks (CNNs) (8-12)-open possibilities for creating a new generation of CAD-like tools.This paper makes several contributions. Primarily, we train and evaluate a set of stro...

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Section: Related Workmentioning

confidence: 99%

Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening

Phang

Park

et al. 2020

IEEE Trans. Med. Imaging

Self Cite

464

397

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“…Therefore, a reliable computer-aided diagnosis (CAD) system is needed to help radiologists make a correct diagnosis. Related research has shown that the CAD system can effectively improve the diagnosis efficiency, reduces the rate of misdiagnosis and the burden of patients [4,5]. In many medical institutions, CAD systems has been used in clinical diagnosis as doctors' reference [6].…”

Section: Introductionmentioning

confidence: 99%

New convolutional neural network model for screening and diagnosis of mammograms

et al. 2020

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Breast cancer is the most common cancer in women and poses a great threat to women's life and health. Mammography is an effective method for the diagnosis of breast cancer, but the results are largely limited by the clinical experience of radiologists. Therefore, the main purpose of this study is to perform two-stage classification (Normal/Abnormal and Benign/ Malignancy) of two-view mammograms through convolutional neural network. In this study, we constructed a multi-view feature fusion network model for classification of mammograms from two views, and we proposed a multi-scale attention DenseNet as the backbone network for feature extraction. The model consists of two independent branches, which are used to extract the features of two mammograms from different views. Our work mainly focuses on the construction of multi-scale convolution module and attention module. The final experimental results show that the model has achieved good performance in both classification tasks. We used the DDSM database to evaluate the proposed method. The accuracy, sensitivity and AUC values of normal and abnormal mammograms classification were 94.92%, 96.52% and 94.72%, respectively. And the accuracy, sensitivity and AUC values of benign and malignant mammograms classification were 95.24%, 96.11% and 95.03%, respectively.

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“…(77) The new deep learning-based techniques may reduce the false-positive rates and provide more accurate diagnoses. Computer-aided diagnosis has had a mixed track record in patient care, dampening enthusiasm for these technologies in clinical settings.…”

Section: Challenges In Implementationmentioning

confidence: 99%

“…Early CAD systems, which were FDA-approved and integrated into direct clinical patient care, predominantly for mammography, often had high sensitivities but also high false-positive rates, leading to inefficiencies and unnecessary testing in clinical practice. (77) The new deep learning-based techniques may reduce the false-positive rates and provide more accurate diagnoses. Another challenge to implementing these technologies in clinical settings has been practice workflow and economic factors.…”

Section: Challenges In Implementationmentioning

confidence: 99%

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

Burns

Yao

Summers

2019

Journal of Bone and Mineral Research

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Artificial intelligence is upending many of our assumptions about the ability of computers to detect and diagnose diseases on medical images. Deep learning, a recent innovation in artificial intelligence, has shown the ability to interpret medical images with sensitivities and specificities at or near that of skilled clinicians for some applications. In this review, we summarize the history of artificial intelligence, present some recent research advances, and speculate about the potential revolutionary clinical impact of the latest computer techniques for bone and muscle imaging. © 2019 American Society for Bone and Mineral Research. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.

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New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence

Cited by 89 publications

References 64 publications

Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening

Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening

New convolutional neural network model for screening and diagnosis of mammograms

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

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