saeed3@mail.usf.edu
ENB building
4202 E. Fowler Avenue
Tampa, FL 33620 USA
Bio
I am a CSE Ph.D. candidate at the University
of South Florida. I am part of the
Computer Vision research group and Radiomics Group at MOFFITT CANCER CENTER . I am advised by Professor:
Dmitry Goldgof and Professor: Lawrence Hall.
Resume
Research Interests
My research interests include Computer vision, Medical Image processing, Machine learning, Deep learning and Stereology.
Projects
- Determining nodule malignancy in National Lung Screening Trial CT screening images using Delta Radiomics
- Determining nodule malignancy in National Lung Screening Trial CT screening images using Combined Radiomics of mutliple screens
- Neurons segmentation and counting in NeuN-stained sections using unbaised stereology and deep learning
- Deep learning ensemeble for Neurons segmentation and counting in NeuN-stained sections
- Active Deep learning for Neurons segmentation and counting in NeuN-stained sections
Papers
- Alahmari, S. S., Goldgof, D., Hall, L., Phoulady, H. A., Patel, R. H., & Mouton, P. R. (2018). Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology. Journal of chemical neuroanatomy.
- Alahmari, S. S., Cherezov, D., Goldgof, D. B., Hall, L. O., Gillies, R. J., & Schabath, M. B. (2018). Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening. IEEE Access, 6, 77796-77806.
- Alahmari, S. S., Goldgof, D., Hall,P., Dave L., Phoulady, H. A., Patel, R. H., & Mouton, P. R. (2018). Iterative Deep Learning Based Unbiased StereologyWith Human-in-the-Loop. International conference of Machine Learning and Application, 2018.
- Alahmari, S. S., Goldgof, D., Hall, L. O., & Mouton, P. R. (2019, October). Automatic Cell Counting using Active Deep Learning and Unbiased Stereology. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 1708-1713). IEEE.
- Dave, P., Goldgof, D., Hall, L. O., Alahmari, S., & Mouton, P. R. (2019). NOVEL STAIN SEPARATION METHOD FOR AUTOMATIC STEREOLOGY OF IMMUNOSTAINED TISSUE SECTIONS. Innovation in Aging, 3(Suppl 1), S256.