Mehdi Alilou

Research Assistant Professor, Biomedical Engineering
Alilou’s research focuses on applying computer vision and machine learning techniques to analyze chest CT scans automatically. The resulting technology aids radiologists and pathologists with automatic detection, quantification, diagnosis, and treatment response prediction of lung cancer and other lung diseases.
Email: mehdi.alilou@case.edu

Education

Ph.D., Computer Science, United Institute of Informatics Problems, National Academy of Sciences (Belarus), 2014

Research Interests

Machine Learning, Computer Vision, Medical Image Analysis

Teaching Interests

Machine learning, image processing, computer vision, programming

Publications

Alilou, M., Prasanna, P., Bera, K., Gupta, A., Rajiah, P., Yang, M., Jacono, F., Velcheti, V., Gilkeson, R., Linden, P., & Madabhushi, A. (2021). A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans. Cancers, 13 (11), 2781.
Prasanna, P., Bobba, V., Figueiredo, N., Sevgi, D., Lu, C., Braman, N., Alilou, M., Sharma, S., Srivastava, S., Madabhushi, A., & Others, A. (2021). Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability. British Journal of Ophthalmology, 105 (8), 1155--1160.
Vaidya, P., Bera, K., Patil, P., Gupta, A., Jain, P., Alilou, M., Khorrami, M., Velcheti, V., & Madabhushi, A. (2020). Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade. Journal for immunotherapy of cancer, 8 (2).
Ernst, F., & Alilou, M. (2020). The Passivity of Low-Temperature Carburized Austenitic Stainless Steel AISI-316L in a Simulated Boiling-Water-Reactor Environment. Journal of Nuclear Materials, 537 , 152197.
Prasanna, P., Bobba, V., Figueiredo, N., Sevgi, D., Lu, C., Braman, N., Alilou, M., Sharma, S., Srivastava, S., Madabhushi, A., & Others, A. (2020). Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability. British Journal of Ophthalmology.
Khorrami, M., Prasanna, P., Gupta, A., Patil, P., Velu, P., Thawani, R., Corredor-Prada, G., Alilou, M., Bera, K., Fu, P., & Others, P. (2020). Changes in CT radiomic features associated with lymphocyte distribution predict overall survival and response to immunotherapy in non-small cell lung cancer. Cancer immunology research, 8 (1), 108.
Khorrami, M., Bera, K., Leo, P., Vaidya, P., Patil, P., Thawani, R., Velu, P., Rajiah, P., Alilou, M., Choi, H., & Others, H. (2020). Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study. Lung Cancer, 142 , 90--97.