Towards Generalizable Reasoned Deep Learning for Efficient Interpretable Medical Image Computing

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Image computing is a key technology area with applications in numerous fields from engineering to medicine. A subset of artificial intelligence (AI) concerned with creating computer systems or machines capable of simulating human vision, the field still grapples with technical challenges arising from the vast variability in imaging data and disparate application-dependent analysis needs. A wave of connectionist machine learning has recently swept the field, in which systems dynamically `learn' to perform a task through training on labelled image examples from huge databases rather than being explicitly programmed. In particular, deep learning (DL), in which multi-layer artificial neural nets that loosely model the human brain are used, has dominated with unprecedented performance levels reported in some applications. Medical image computing poses unique technical challenges to automated analysis that limited success in this area. Quite distinct from pure cognitive vision applications, such as autonomous driving, analysis and interpretation of medical image data involves highly specialized processes connected to complex knowledge bases. Hence, despite their apparent power and universal applicability, current data-driven DL models often result in superficial learning with alarming reports of model fragility and unexpected failures, overfitting to noise and underfitting to meaningful data, poor generalizability and low performance on new data, as well as impracticality of required training databases and prohibitive cost of labelling. This grant will advance DL methods towards `deep reasoning' to enable reliable and safe AI for critical data analysis such as medical image computing. Specifically, the proposed technical methods will evolve data-driven DL models beyond visual perception (seeing) towards integrating logic and context based reasoning (understanding, extrapolating) in order to achieve `common sense' solutions that observe physical realities and incorporate domain knowledge. My work will result in more generalizable, interpretable, efficient and reasoned learning models that can self-test and self-improve, ensuring real life utility and significantly improving the prospects of successful adoption in areas of strategic importance to Canadians. Automation of heterogenous complex medical image data analysis enabled by the proposed technical methods will have significant socio-economic impact including overcoming poor physician to patient ratio, reducing healthcare costs and delays, increasing accuracy and efficiency in diagnostics and care delivery, and enabling new findings and opportunities in health research. My research will train a diverse group of future experts and leaders in AI, a field where severe shortages in much needed talent exist. It will also help ensure Canada remains a world leader in AI research and a major contributor to the `DL revolution'.