Differentiable Programming for Computer Vision and Medical Image Analysis

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Deep learning has emerged as a strong method with empirical evidence to deliver accurate learning models for various computer vision applications. The success behind deep learning seems to be supervised learning that requires lots and lots of tagged image and video data. The requirement to have lots of training data with expert-created labels or tags pose a particularly challenging situation for medical image analysis applications, where scarcity of training data is common. Over the years, some methods, such as transfer learning have emerged to mitigate this issue. However, none of these techniques can adequately address lack of training data. The proposed research program will explore an alternative to the exiting techniques and will make use of prior and domain knowledge along with the power of deep learning to address the scarcity of labeled training data. As a technical solution, the program will rely on differentiable programming to mix traditional computer vision algorithms with deep learning methods, where the prior knowledge can be included in the traditional methods. Differentiable programming refers to gradient descent based optimization that relies on the differentiability of all the functions used in a data processing pipeline. However, most traditional computer vision methods include functions or processes that are not differentiable. Thus, the research further proposes to overcome this difficulty by using bypass neural networks to approximate non-differentiable functional modules. Using several use cases, the proposed research also demonstrates that the scope extends further beyond the lack of training data. For example, an end-to-end detection-tracking system for multi-object tracking can be cast into the proposed optimization framework. The research program will have ample opportunity to provide broad training to students in computer vision, medical image analysis, theoretical and statistical analysis of learning algorithms that fit well into Canada's commitment to artificial intelligence research. The proposed research can create a new and significant family of learning algorithms in computer vision and image analysis research.