Past talk · AI/ML Seminar Series
AI-based multimodal data fusion for outcome prediction in oncology
Assistant Professor, Department of Pathology, School of Medicine, University of California, Irvine
- Date & time
- Monday, October 28, 2024 · 1:00 PM
- Location
- Donald Bren Hall 4011
Abstract
In oncology, the patient state is characterized by a spectrum of diverse medical data, each providing unique insights. The vast amount of data, however, makes it difficult for experts to adequately assess patient prognosis under the multimodal context. We present a deep learning-based multimodal framework for integration of radiology, histopathology, and genomics data to improve patient outcome prediction. The framework does not require annotations, tumor segmentation, or hand-crafted features and can be easily applied to larger cohorts and diverse disease models. The feasibility of the model is tested on two external independent cohorts, including glioma and non-small cell lung cancer, indicating benefits of multimodal data integration for patient risk stratification, outcome prediction, and prognostic biomarker exploration.
About the speaker
Jana Lipkova is an Assistant Professor at the University of California Irvine, in Dept. of Pathology and also in Dept. of Biomedical Engineering. She completed her postdoctoral fellowship in the AI for Pathology group under the guidance of Faisal Mahmood at Harvard Medical School. Prior to her postdoc, she earned a PhD in computer-aided medical procedures in the radiology department at Technical University in Munich. Jana's research lab, called OctoPath, focuses on developing AI methods for diagnosis, prognosis, and treatment optimization in histopathology and beyond.