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4 April 2022 Assessment of patch-based mammogram denoising methods using virtual clinical trials and deep learning: trade-off between denoising strength and preservation of structural details
Vincent Dong, Tristan D. Maidment, Lucas R. Borges, Bruno Barufaldi, Susan Ng, Andrew D. A. Maidment
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Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging; 120311W (2022) https://doi.org/10.1117/12.2612900
Event: SPIE Medical Imaging, 2022, San Diego, California, United States
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Abstract
Noiseless digital mammograms (DM) are unobtainable in clinical screening environments, limiting the development of deep learning-based (DL) denoising applications. Virtual clinical trials (VCTs) allow the precise simulation of noise levels in DM images for controlled training of DL models. We evaluated a set of DL denoising models, trained using VCT data, that showcases the trade-offs between denoising strength and fine structure preservation. Our results show that metrics, such as peak signal-to-noise ratio (PSNR), are improved with the use of our trained residual convolutional neural network. This quantifiable improvement indicates that our proposed DL methodology can accurately denoise simulated mammograms.
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Vincent Dong, Tristan D. Maidment, Lucas R. Borges, Bruno Barufaldi, Susan Ng, and Andrew D. A. Maidment "Assessment of patch-based mammogram denoising methods using virtual clinical trials and deep learning: trade-off between denoising strength and preservation of structural details", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 120311W (4 April 2022); https://doi.org/10.1117/12.2612900
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KEYWORDS
Denoising
Mammography
Clinical trials
Breast
Image quality
Spatial frequencies
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