Aimilia Gastounioti, Ph.D.

Research Associate

Aimilia Gastounioti, Ph.D.Computational Biomarker Imaging Group  (CBIG)
Center for Biomedical Image Computing & Analytics (CBICA)
Department of Radiology
Perelman School of Medicine
University of Pennsylvania



Richards Labs, Suite 700D
3700 Hamilton Walk
Philadelphia, PA 19104



Research Interests

breast cancer risk, translational biomedical imaging research, computational imaging phenotypes, machine learning, deep learning

Research Summary

I am an electrical and computer engineer with a 10-year research experience in biomedical imaging analytics. My research interests lie in translational biomedical imaging research, artificial intelligence and data-intensive biomedical science with a primary focus on cancer imaging phenotypes and machine learning related to cancer risk-prediction. I have co-authored 22 journal articles, 3 book chapters, 26 conference proceedings papers, as well as 22 abstracts in premier scientific meetings. I am currently leading a Susan G. Komen fellowship grant aiming to advance imaging phenotyping of breast cancer risk via deep learning technologies and am co-leading a pilot grant from the Penn Institute for Translational Medicine and Therapeutics that is using deep learning methods to understand genetic associations of racial differences in mammographic patterns. My research work has been awarded by the RSNA and the IEEE EMB Greece Chapter, and has also been included in Research Highlights of the IEEE Journal of Biomedical Health Informatics, the SPIE Medical Imaging Conference and the AACR Special Conference on Convergence: Artificial Intelligence, Big Data, and Prediction in Cancer. I am an Associate member of the Institute for Translational Medicine and Therapeutics (ITMAT) at UPenn and the American Association for Cancer Research (AACR).


Link to google scholar here.

Representative Publications

A. Gastounioti and D. Kontos, “Is it time to get rid of black boxes and cultivate trust in AI?,” Invited Commentary, Radiology: Artificial Intelligence, 2(3): e200088, 2020,

M. McNitt-Gray, S. Napel, A. Jaggi, S.A. Mattonen, L. Hadjiiski, M. Muzi, D. Goldgof, Y. Balagurunathan, L.A. Pierce, P.E. Kinahan, E.F. Jones, A. Nguyen, A. Virkud, H.P. Chan, N. Emaminejad, M. Wahi-Anwar, M. Daly, M. Abdalah, H. Yang, L. Lu, W. Lv, A. Rahmim, A. Gastounioti, S. Pati, S. Bakas, D. Kontos, B. Zhao, J. Kalpathy-Cramer, and K. Farahani, “Standardization in quantitative imaging: A multi-center comparison of radiomic features from different software packages on digital reference objects and patient datasets,” Tomography (Special Issue on NCI’s Quantitative Imaging Network), 6(2): 118–128, 2020,

R. D. Chitalia, J. Rowland, E. McDonald, L. Pantalone, E. Cohen, A. Gastounioti, M. Feldman, M. D. Schnall, E. F. Conant, and D. Kontos, "Imaging phenotypes of breast cancer heterogeneity in preoperative breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) scans predict 10-year recurrence," Clinical Cancer Research, 26(4): 862-869, 2020.

Gastounioti, Aimilia, Kasi, Christine Damases, Scott, Christopher G, Brandt, Kathleen R, Jensen, Matthew R, Hruska, Carrie B, Wu, Fang F, Norman, Aaron D, Conant, Emily F, Winham, Stacey JEvaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction. Radiology Page: 192509, 2020.

Gastounioti, A., McCarthy, A. M., Pantalone, L., Synnestvedt, M., Kontos, D., Conant, E. F.: Effect of Mammographic Screening Modality on Breast Density Assessment: Digital Mammography versus Digital Breast Tomosynthesis. Radiology 291(2): 320-327, 2019.

Davatzikos, C., Sotiras, A., Fan, Y., Habes, M., Erus, G., Rathore, S., Bakas, S., Chitalia, R., Gastounioti, A., Kontos, D.Precision diagnostics based on machine learning-derived imaging signatures. 64: 49-61, 2019.

Kontos, Despina, Winham, Stacey J., Oustimov, Andrew, Pantalone, Lauren, Hsieh, Meng-Kang, Gastounioti, Aimilia, Whaley, Dana H., Hruska, Carrie B., Kerlikowske, Karla, Brandt, Kathleen, Conant, Emily F., Vachon, Celine M.: Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment. Radiology. Radiological Society of North America, Page: 180179, 2018.

Gastounioti, A., Oustimov, A., Hsieh, M. K., Pantalone, L., Conant, E. F., Kontos, D.: Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk. 25(8): 977-984, 2018.

Gastounioti, A., Hsieh, M. K., Cohen, E., Pantalone, L., Conant, E. F., Kontos, D.: Incorporating Breast Anatomy in Computational Phenotyping of Mammographic Parenchymal Patterns for Breast Cancer Risk Estimation. Sci Rep 8(1): 17489, 2018.

Davatzikos, C., Rathore, S., Bakas, S., Pati, S., Bergman, M., Kalarot, R., Sridharan, P., Gastounioti, A., Jahani, N., Cohen, E., Akbari, H., Tunc, B., Doshi, J., Parker, D., Hsieh, M., Sotiras, A., Li, H., Ou, Y., Doot, R. K., Bilello, M., Fan, Y., Shinohara, R. T., Yushkevich, P., Verma, R., Kontos, D.: Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. J Med Imaging (Bellingham) 5(1): 011018, 2018.

Conant, Emily F, Keller, Brad M, Pantalone, Lauren, Gastounioti, Aimilia, McDonald, Elizabeth S, Kontos, Despina: Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures. Radiology Page: 161286, 2017.

Gastounioti, Aimilia, Oustimov, Andrew, Keller, Brad M, Pantalone, Lauren, Hsieh, Meng-Kang, Conant, Emily F, Kontos, Despina: Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations. Medical physics 43(11): 5862-5877, 2016.

Gastounioti, A., Conant, E. F., Kontos, D.: Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment. Breast Cancer Res 18(1): 91, 2016.