معرفی
Mirabela Rusu serves as an Assistant Professor in the Department of Biomedical Data Science at Stanford University School of Medicine. Her academic responsibilities span multiple departments including Radiology, Computer Science, and Biomedical Data Science, where she teaches core courses such as Introduction to Biomedical Informatics Research Methodology (BIOE 212/GENE 212/CS 272/BMDS 212) and Computational Methods for Biomedical Image Analysis (CS 235/BMP 260/RAD 260/BMDS 260), along with extensive independent study supervision across radiology and computational medicine.
Her research program focuses on artificial intelligence applications in medical imaging, particularly for prostate cancer diagnostics. She develops advanced AI frameworks for multimodal image analysis (MRI, ultrasound, histopathology), with specializations in cancer detection, image registration, and segmentation. Current work emphasizes clinical translation through minimal-data training approaches, differential privacy for medical AI, and bridging radiology-pathology correlations to improve diagnostic accuracy beyond human performance levels.
Analysis of her 15 most recent publications (2023-2025) reveals a dominant research trajectory in prostate cancer imaging AI, characterized by three key trends: multimodal fusion techniques (MRI-TRUS integration), clinical validation against radiologist benchmarks, and development of annotation-efficient models. Her work consistently addresses real-world clinical challenges including prostate cancer localization, treatment response prediction, and EHR data extraction using cutting-edge methods like diffusion probabilistic models and agentic LLMs.
Dr. Rusu actively mentors students through six independent study courses (RAD 199/299, CME 400/291, RAD 399, BMDS 295) covering undergraduate research, directed reading, and graduate thesis supervision. Her teaching philosophy integrates research methodology with practical computational skills, preparing students for AI-driven healthcare innovation while advancing her lab's mission to transform cancer diagnostics through computational approaches.