Limited-supervision for efficient medical image understanding

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Motivation: Deep learning models are driving progress in most visual recognition tasks, having an enormous and valuable potential in strategic domains such as health-care, autonomous driving or security. While these techniques have achieved outstanding performance when full-supervision is available, there exist two major obstacles to deploy these models in practical applications: poor generalization performance on new tasks and potential loss of learned knowledge when training on sequential tasks. Ideally, expanding a trained model to novel classes would require collecting and labeling additional data for new categories and restart the training procedure on the entire novel dataset. Nevertheless, this scenario is unrealistic. Thus, there is a need to bring current learning algorithms to a whole new level where models can quickly adapt to new tasks (i.e., with few labeled samples) and avoid knowledge forgetting. Research objectives and methodology: This research program will contribute to develop novel learning strategies for visual applications that address these two key challenges on deep models, with a primary focus on semantic segmentation. We intend to leverage unlabeled image data and prior domain knowledge, which have been underestimated in current approaches. This will enhance the representation power of deep models, leading to more generalizable features when few labeled data is available. To reduce the catastrophic interference between tasks -learned sequentially-, we will investigate novel attention mechanisms and the use of meta-memories, forcing the network to focus on class-agnostic parameters which will be retained across tasks. This research program will focus on medical images, given the unique challenges they bring to machine learning methods (e.g., complex shape, high variability, etc), and their tremendous potential for healthcare. Impact: I expect that this program will lead to robust learning methods to understand and interpret the content of medical imaging data in different challenging scenarios, such as low labeled data regime and sequential training, e.g., in decentralized systems. In the long-term, I aim at transferring specific human behaviour to computers, from an image interpretation perspective, so that they can efficiently learn from few samples and avoid significant memory losses when learning different tasks sequentially. Robust interpretation of these images will provide invaluable support for diagnosis, treatment and follow-up of many diseases. Even though devised methods can be applied to a breadth of applications, we will prioritize neurology, cardiology and oncology, which have a high economical and social impact and for which we have a considerable experience. Beyond the application domain adopted in this program, these novel learning strategies will make an impact on the area of semantic segmentation, particularly when full annotations are scarce, in a broad span of disciplines.