Invited Talk – Prof. Dr. Mirabela Rusu (Stanford University): From pathology to ultrasound via MRI: multimodal learning for cancer detection, Tuesday, June 24th, 2025, 16:30 CET
It’s a great pleasure to welcome Prof. Dr. Mirabela Rusu, the head of the Personalized Integrative Medicine Laboratory at Stanford University to our lab!
Title: From pathology to ultrasound via MRI: multimodal learning for cancer detection
Date: Tuesday, June 24th, 2025, 16:30 CET
Location: Nikolaus-Fiebiger-Str. 10, 91058 Erlangen, Lecture Hall C1 (13301.U1.0171 Chemikum)
Abstract:
Clinical care is inherently multimodal, with medical image data collected throughout the patient’s journey. For example, a patient at risk of cancer will undergo an ultrasound-guided biopsy, and when available with MRI revealing regions to be targeted due to higher risk to harbor aggressive disease. This biopsy procedure seeks to collect tissue samples for pathology and will inform treatment strategies for best outcomes. This common scenario provides unique opportunities for Artificial Intelligence (AI) methods to effectively integrate multimodal data, and learn imaging signatures in patients with known outcomes, to enable early cancer detection for patients at risk. This presentation focuses on showcasing some of our AI methods that bridge the gap between highly informative modalities, e.g., MRI, and lower resolution modalities, e.g., ultrasound. These methods rely on multimodal image registration, image feature fusion, or integration of patient-specific data and population-specific information and rely on AI approaches for effective integration. While the learning is done with multiple imaging modalities, the inference requires only the low-resolution modality, e.g., ubiquitous conventional ultrasound, with applications in low-resource settings.
Short Bio: Mirabela Rusu is an Assistant Professor in the Department of Radiology, and, by courtesy, Department of Urology and Biomedical Data Science, at Stanford University, where she leads the Personalized Integrative Medicine Laboratory (PIMed). The PIMed Laboratory has a multi-disciplinary direction and focuses on developing analytic methods for biomedical data integration, with a particular interest in multimodal fusion, e.g., radiology-pathology fusion to facilitate radiology image labeling, or MRI-ultrasound for guiding procedure. These fusion approaches allow the downstream training of advanced multimodal machine learning for cancer detection and subtype identification at pixel-level, and have been applied in oncologic (prostate, breast, kidney) and non-oncologic applications. Further details are available online at https://profiles.stanford.edu/mirabela-rusu.
