Computational Learning
Work description
Breast cancer (BC) is now the cancer with the highest incidence worldwide and a leading cause of cancer fatalities. Current standard treatment involves surgery combined with radiotherapy, however, 2/3 of early-detected BC tumors are clinically unpalpable. These require invasive, less accurate localization procedures for a conservative therapeutic approach. To succeed, a fundamental scientific problem must first be solved: how does the breast, a highly deformable organ, change shape when a patient is in different postures, while scanned using different medical imaging modalities? In this project, we aim to develop a novel, hybrid in silico/physics-informed machine learning approaches, for generation of artificial data based on generative models and for multimodal data fusion. The result will be cutting-edge, high-fidelity digital breast models with breast magnetic resonance imaging pose transformation from prone to supine, to predict tumor location.
Minimum profile required
Experience in Computer Vision and machine learning.
Preference factors
Experience in research projects, and writing of scientific papers.
Application Period
Since 21 Nov 2024 to 04 Dec 2024
Centre
Telecommunications and Multimedia