Optimization and learning algorithms for medical image interpretation

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General context and problematic: The overwhelming growth of large-scale image data acquired and stored everyday brings unprecedented opportunities for accurate predictive models, better-guided decisions and new insights and discoveries, with the enormous potential to impact strategic areas such as health care, security, social media, robotics and autonomous systems, remote sensing and manufacturing. State-of-the-art image interpretation algorithms require very large amounts of reliable training data, i.e., accurately labeled (annotated) images built with extensive human labour and expertise. Such a comprehensive supervision is a major impediment in a breadth of application areas, e.g., medical image analysis. This calls for bringing current algorithms to a whole new level of automation, scalability and accuracy, leveraging the available large-scale amounts of unlabeled images and the uncertain (noisy) knowledge that might be associated with the images (e.g., text). Objectives: Our research program focuses on novel mathematical models and computational methods for weakly supervised semantic image segmentation and categorization, the two key problems in image interpretation systems. Following on our expertise, we intend to pursue optimization-based formulations, which leverage large-scale, mostly unlabeled image data with important and complex prior knowledge that has been either omitted or oversimplified in current methods. Specific technical objectives are: (i) defining novel constraints, which embody domain knowledge, thereby mitigating the lack/uncertainty of data annotations; (ii) designing novel, approximation-based strategies for optimizing the ensuing difficult problems; and (iii) evaluating and validating our investigations on medical images, for their high variability and complexity, the challenges they bring to machine learning algorithms (e.g., the lack of annotations), and their great promises for advancing health-care practices/research. While this research can serve a breadth of clinical applications, we intend to prioritize two domains of high impact on the economy and society: neurology and oncology. Significance: With formulations integrating domain knowledge and large-scale data, as well as advanced optimization expertise, this research promises to deliver internationally competitive algorithms (in terms of automation, precision, speed and robustness). The scope goes far beyond computer-vision and medical-imaging applications, with a potential impact in the general, wide-interest subject of weakly supervised learning, and in various application disciplines. In medical imaging alone, the potential is huge given the rich domain knowledge (e.g., the anatomy and radiology text reports). In this application domain, powerful weakly supervised algorithms promise to impact health care research (e.g., the understanding of complex diseases) and practices (e.g., disease early detection, diagnosis, monitoring, treatment and follow-up).