Sahar Almahfouz Nasser is a postdoctoral fellow working on projects related to mitosis detection, h-score estimation, and federated learning. Her research interests include computer vision, deep learning, graph neural networks, medical image analysis, histopathology, radiology and image-guided surgery, as well as image registration, matching, and segmentation. She also is involved in keypoint and object detection related to machine learning.
Prior to joining the lab, Dr. Nasser worked on various projects, including the following:
1. The Aesthetic Evaluation of Breast Cancer Conservative Treatment
2. Keypoint Detection and Image Matching for Retinal Images
3.Leveraging Segmentation to Improve Medical Image Registration
4. Transforming Breast Cancer Diagnosis: Towards Real-Time Ultrasound to Mammogram Conversion for Cost-Effective Diagnosis
5. Deep Learning Methods for Mitosis Domain Generalization
6. Medical Image Registration for Ultrasound-MRI Fusion in Prostate Diagnostics and Surgery
7. Frame-to-Volume Ultrasound Registration
8. Weakly Supervised Semantic Attentive Brain Tumor Sequence Registration
9. Perceptual CGAN for MRI Super-Resolution
10. Multi-Modal Information Fusion for Classification of Kidney Abnormalities
11. Deep Wavelet for Medical Image Super-Resolution
12. Deep Learning for Natural Image Captioning
13. Single Test Image-based Automated Machine Learning System for Distinguishing between Trait and Diseased Blood Samples
Dr. Nasser earned her PhD in electrical engineering from IIT-Bombay, with a doctoral dissertation on cross-domain image adaptation and matching, where she also earned a master's of technology degree in biomedical engineering. She completed her undergraduate degree in biomedical engineering at Damascus University.