Comprehensive assessment of image quality in synthetic and digital mammography: a quantitative comparison

Barca, Patrizio; Lamastra, Rocco; Aringhieri, Giacomo; Tucciariello, Raffaele M.; Traino, Antonio Claudio; Fantacci, Maria Evelina

doi:10.1007/s13246-019-00816-8

Cited by 9 publications

(9 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the experiments, we use L = 50 and c = 1 which we found to perform well for the considered use case. L should be large enough so that the according step size ε = c L will expectedly lead to a lowering of the midpoint in the update (2). The parameter c determines the length of the path leading to x ′ .…”

Section: Omig Explainabilitymentioning

confidence: 99%

“…in IQA but also in a broader context) that possesses a natural 'direction' . Given such a direction, one can choose a value for R that is similar to that in (1) in order to point in (the inverse of) this direction and then update x to create an x ′ according to (2). Finally, we can define the OMIG explainability via (3), which gives information on how x must be modified to shift the prediction toward the natural direction.…”

Section: Limitations and Outlookmentioning

confidence: 99%

“…This main goal must be achieved with the lowest possible radiation dose. A variety of phantoms for IQA in mammography have been proposed [2]. The method we take as a reference here was recommended by European Guidelines [3,4] and is based on the automated readout of the contrast-detail curves (CDCs) from a set of recorded images of a CDMAM 1 phantom [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Explainability for deep learning in mammography image quality assessment

Amanova

Martin

Elster

2022

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

The application of deep learning has recently been proposed for the assessment of image quality in mammography. It was demonstrated in a proof-of-principle study that the proposed approach can be more efficient than currently applied automated conventional methods. However, in contrast to conventional methods, the deep learning approach has a black-box nature and, before it can be recommended for the routine use, it must be understood more thoroughly. For this purpose, we propose and apply a new explainability method: the oriented, modified integrated gradients (OMIG) method. The design of this method is inspired by the integrated gradients method but adapted considerably to the use case at hand. To further enhance this method, an upsampling technique is developed that produces high-resolution explainability maps for the downsampled data used by the deep learning approach. Comparison with established explainability methods demonstrates that the proposed approach yields substantially more expressive and informative results for our specific use case. Application of the proposed explainability approach generally confirms the validity of the considered deep learning-based mammography image quality assessment method. Specifically, it is demonstrated that the predicted image quality is based on a meaningful mapping that makes successful use of certain geometric structures of the images. In addition, the novel explainability method helps us to identify the parts of the employed phantom that have the largest impact on the predicted image quality, and to shed some light on cases in which the trained neural networks fail to work as expected. While tailored to assess a specific approach from deep learning for mammography image quality assessment, the proposed explainability method could also become relevant in other, similar deep learning applications based on high-dimensional images.

show abstract

Section: Omig Explainabilitymentioning

confidence: 99%

Section: Limitations and Outlookmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Explainability for deep learning in mammography image quality assessment

Amanova

Martin

Elster

2022

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…7 The image has an intrinsically different appearance and low overall resolution and noise properties compared to the FFDM image. 8,9 Clinical studies have shown that C-view + DBT have a performance similar to that of standard FFDM + DBT. 27,28,39 Intelligent 2D was newly developed by Hologic to further improve the performance of C-view.…”

Section: Introductionmentioning

confidence: 99%

“…29,35,36 Besides, the C-view image provides poor overall resolution and noise properties compared to the FFDM image. 30,31,40,41 More importantly, large-scale clinical studies reported that more breasts' density is categorized as non-dense than dense when using C-view + DBT compared to using FFDM + DBT, 32,33,37,38 probably due to inherently different visual appearance between the C-view image and FFDM image. Since breast density has both imaging and risk implications 34 and is an important component of mammography reports and BI-RADS category classification, the C-view image may result in an inconsistent mammography report to FFDM image and unreliable risk assessment,which increases the recall of patients.…”

Section: Introductionmentioning

confidence: 99%

Multi‐scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model‐based perceptual similarity

Jiang

Zhou

et al. 2022

Medical Physics

View full text Add to dashboard Cite

Synthetic digital mammogram (SDM) is a 2D image generated from digital breast tomosynthesis (DBT) and used as a substitute for a full-field digital mammogram (FFDM) to reduce the radiation dose for breast cancer screening. The previous deep learning-based method used FFDM images as the ground truth, and trained a single neural network to directly generate SDM images with similar appearances (e.g., intensity distribution, textures) to the FFDM images. However, the FFDM image has a different texture pattern from DBT. The difference in texture pattern might make the training of the neural network unstable and result in high-intensity distortion, which makes it hard to decrease intensity distortion and increase perceptual similarity (e.g., generate similar textures) at the same time. Clinically, radiologists want to have a 2D synthesized image that feels like an FFDM image in vision and preserves local structures such as both mass and microcalcifications (MCs) in DBT because radiologists have been trained on reading FFDM images for a long time, while local structures are important for diagnosis. In this study, we proposed to use a deep convolutional neural network to learn the transformation to generate SDM from DBT. Method: To decrease intensity distortion and increase perceptual similarity, a multi-scale cascaded network (MSCN) is proposed to generate low-frequency structures (e.g., intensity distribution) and high-frequency structures (e.g., textures) separately. The MSCN consist of two cascaded sub-networks: the first sub-network is used to predict the low-frequency part of the FFDM image; the second sub-network is used to generate a full SDM image with textures similar to the FFDM image based on the prediction of the first sub-network. The meansquared error (MSE) objective function is used to train the first sub-network, termed low-frequency network, to generate a low-frequency SDM image. The gradient-guided generative adversarial network's objective function is to train the second sub-network, termed high-frequency network, to generate a full SDM image with textures similar to the FFDM image. Results: 1646 cases with FFDM and DBT were retrospectively collected from the Hologic Selenia system for training and validation dataset, and 145 cases with masses or MC clusters were independently collected from the Hologic Selenia system for testing dataset. For comparison, the baseline network has the same architecture as the high-frequency network and directly generates a full SDM image. Compared to the baseline method, the proposed MSCN improves the peak-to-noise ratio from 25.3 to 27.9 dB and improves the Gongfa Jiang, Zilong He, and Yuanpin Zhou contributed equally to this work.

show abstract

Prospective Comparison of Synthesized Mammography with DBT and Full-Field Digital Mammography with DBT Uncovers Recall Disagreements That may Impact Cancer Detection

Huang

Hess

et al. 2022

Academic Radiology

View full text Add to dashboard Cite

Comprehensive assessment of image quality in synthetic and digital mammography: a quantitative comparison

Cited by 9 publications

References 25 publications

Explainability for deep learning in mammography image quality assessment

Explainability for deep learning in mammography image quality assessment

Multi‐scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model‐based perceptual similarity

Prospective Comparison of Synthesized Mammography with DBT and Full-Field Digital Mammography with DBT Uncovers Recall Disagreements That may Impact Cancer Detection

Contact Info

Product

Resources

About