Dr. Anne Marie McCarthy Profile

Dr. Anne Marie McCarthy

at Univ. of Pennsylvania

SPIE Involvement:

Author

Publications (3)

Proceedings Article | 29 May 2024 Paper

Explainable radiomics to characterize breast density and tissue complexity: preliminary findings

Vincent Dong, Bruno Barufaldi, Walter Mankowski, Telmo Silva Filho, Anne Marie McCarthy, Despina Kontos, Andrew D. Maidment

Proceedings Volume 13174, 131741L (2024) https://doi.org/10.1117/12.3027032

KEYWORDS: Radiomics, Cross validation, Breast density, Feature selection, Feature extraction

Read Abstract +

Proceedings Article | 29 May 2024 Paper

Sat2Nu: a modular deep learning pipeline for converting fat-suppressed breast MRIs to nonfat-suppressed images with foreseeable applications in abbreviated breast MRI

Kalina Slavkova, Nehal Doiphode, Chunrui Zou, Alex Anh-Tu Nguyen, Arunima Sharma, Anne Marie McCarthy, Despina Kontos

Proceedings Volume 13174, 1317416 (2024) https://doi.org/10.1117/12.3027028

KEYWORDS: Education and training, Magnetic resonance imaging, Breast, Batch normalization, Deep learning, Spatial resolution, Image segmentation, Spatial learning, Reproducibility, Breast cancer

Read Abstract +

While standard of care breast MRI primarily includes T1-weighted (T1w) fat-suppressed images, nonfat-suppressed images are not always included but may be needed to detect fat necrosis or fatty lesions. With the advent of abbreviated MRI to increase the accessibility of MRI for breast cancer screening, it is unlikely that imaging exams will contain both fat- and nonfat-suppressed images. Additionally, nonfat-suppressed images are integral for downstream quantitative analyses. Deep learning has seen increased use in medical imaging for contrast synthesis; however, there is limited work in the breast. This study aims to develop a reproducible, modular deep learning framework called Sat2Nu for generating nonfat-suppressed images from fat-suppressed inputs with limited training data. We retrospectively analyzed 2D slices from 643 bilateral sagittal T1w MRI screening exams with corresponding fat- and nonfat-suppressed scans from the University of Pennsylvania. One central slice was selected from each breast to yield 1,286 2D images. We trained a U-Net architecture on the entire dataset, where nonfat-suppressed images served as the ground truth. We randomly selected 20% of the data as an in-distribution validation set. The normalized root mean square error (NRMSE) and structural similarity index (SSIM) were used as performance metrics. We achieved a training NRMSE and SSIM of 0.143 and 0.855, respectively. Validation metrics on the in-distribution validation set were, respectively, 0.099 and 0.889. In conclusion, our preliminary results demonstrate representational capacity for the network to learn nonfat-suppressed contrast from fat-suppressed MRIs, which could develop into a promising solution for generating missing scans in the abbreviated setting and for downstream quantitative analyses dependent on nonfat-suppressed images. Current efforts include external validation and investigating other generative networks and loss functions for improving generalizability. Importantly, we are focusing on designing a reproducible pipeline that would allow future users to easily implement different architectures.

Proceedings Article | 13 July 2022 Paper

External validation of an AI-driven breast cancer risk prediction model in a racially diverse cohort of women undergoing mammographic screening

Aimilia Gastounioti, Mikael Eriksson, Eric Cohen, Walter Mankowski, Lauren Pantalone, Anne Marie McCarthy, Despina Kontos, Per Hall, Emily Conant

Proceedings Volume 12286, 1228617 (2022) https://doi.org/10.1117/12.2627140

KEYWORDS: Breast cancer, Artificial intelligence, Breast, Digital mammography, Machine learning

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years