Deep Learning in Mammography

Becker, Anton S.; Marcon, Magda; Ghafoor, Soleen; Wurnig, Moritz C.; Frauenfelder, Thomas; Boss, Andreas

doi:10.1097/rli.0000000000000358

Cited by 295 publications

(75 citation statements)

References 22 publications

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“…If ROI annotations were widely available in mammography databases then established object detection and classification methods such as the region-based convolutional neural network (R-CNN) [13] and its variants [14][15][16] could be readily applied. However, approaches that require ROI annotations [17][18][19][20][21][22][23][24][25][26][27] often cannot be transferred to large mammography databases that lack ROI annotations, which are laborious and costly to assemble. Indeed, few public mammography databases are annotated [28].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Shen

Margolies

Rothstein

et al. 2019

Sci Rep

652

428

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The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging problems. Here, we develop a deep learning algorithm that can accurately detect breast cancer on screening mammograms using an “end-to-end” training approach that efficiently leverages training datasets with either complete clinical annotation or only the cancer status (label) of the whole image. In this approach, lesion annotations are required only in the initial training stage, and subsequent stages require only image-level labels, eliminating the reliance on rarely available lesion annotations. Our all convolutional network method for classifying screening mammograms attained excellent performance in comparison with previous methods. On an independent test set of digitized film mammograms from the Digital Database for Screening Mammography (CBIS-DDSM), the best single model achieved a per-image AUC of 0.88, and four-model averaging improved the AUC to 0.91 (sensitivity: 86.1%, specificity: 80.1%). On an independent test set of full-field digital mammography (FFDM) images from the INbreast database, the best single model achieved a per-image AUC of 0.95, and four-model averaging improved the AUC to 0.98 (sensitivity: 86.7%, specificity: 96.1%). We also demonstrate that a whole image classifier trained using our end-to-end approach on the CBIS-DDSM digitized film mammograms can be transferred to INbreast FFDM images using only a subset of the INbreast data for fine-tuning and without further reliance on the availability of lesion annotations. These findings show that automatic deep learning methods can be readily trained to attain high accuracy on heterogeneous mammography platforms, and hold tremendous promise for improving clinical tools to reduce false positive and false negative screening mammography results. Code and model available at: https://github.com/lishen/end2end-all-conv .

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Section: Introductionmentioning

confidence: 99%

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Shen

Margolies

Rothstein

et al. 2019

Sci Rep

652

428

View full text Add to dashboard Cite

show abstract

“…Among the promises of AI are that it may increase the productivity of medical radiation professionals, reduce the cost of imaging, and perhaps even increase the accuracy of diagnostic imaging or lead to new biomarker discovery [1,[6][7][8]. With that said, existing AI applications tend to be task specific and work toward addressing predefined problems [2].…”

Section: Ai and Medical Radiation Sciencesmentioning

confidence: 99%

AI and Ethics in Medical Radiation Sciences

Smith

Bean

2019

Journal of Medical Imaging and Radiation Sciences

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“…Although this approach is of relatively high computation cost compared to similar ones, but results indicate a notable detection quality improvement. A comparison study using relatively large amount of patient data is conducted in [8]. Several networks are evaluated and results indicate that GoogLeNet architecture can achieve highest accuracy value from area under the curve (AUC) measure measured value.…”

Section: Related Workmentioning

confidence: 99%

“…Convolutional neural networks in particular leads to a remarkable impact in image analysis and understanding especially in image segmentation, classification and analysis [4]. Several models employ deep learning are already developed for diagnosis and identification of breast cancer through analysis of digital mammography [5][6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach for breast cancer diagnosis

Rashed

Seoud

2019

Proceedings of the 2019 8th International Conference on Software and Information Engineering

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Breast cancer is one of the leading fatal disease worldwide with high risk control if early discovered. Conventional method for breast screening is x-ray mammography, which is known to be challenging for early detection of cancer lesions. The dense breast structure produced due to the compression process during imaging lead to difficulties to recognize small size abnormalities. Also, inter-and intra-variations of breast tissues lead to significant difficulties to achieve high diagnosis accuracy using hand-crafted features. Deep learning is an emerging machine learning technology that requires a relatively high computation power. Yet, it proved to be very effective in several difficult tasks that requires decision making at the level of human intelligence. In this paper, we develop a new network architecture inspired by the U-net structure that can be used for effective and early detection of breast cancer. Results indicate a high rate of sensitivity and specificity that indicate potential usefulness of the proposed approach in clinical use.

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Deep Learning in Mammography

Abstract: Current state-of-the-art artificial neural networks for general image analysis are able to detect cancer in mammographies with similar accuracy to radiologists, even in a screening-like cohort with low breast cancer prevalence.

Cited by 295 publications

References 22 publications

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Deep Learning to Improve Breast Cancer Detection on Screening Mammography

AI and Ethics in Medical Radiation Sciences

Deep learning approach for breast cancer diagnosis

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