Radiotherapy is one of the main ways head and neck cancers are treated; radiation is used to kill cancerous cells and prevent their recurrence.Complex treatment planning is required to ensure that enough radiation is given to the tumour, and little to other sensitive structures (known as organs at risk) such as the eyes and nerves which might otherwise be damaged. This is especially difficult in the head and neck, where multiple at-risk structures often lie in extremely close proximity to the tumour. It can take radiotherapy experts four hours or more to pick out the important areas on planning scans (known as segmentation). This research will focus on applying machine learning algorithms to automatic segmentation of head and neck planning computed tomography (CT) and magnetic resonance imaging (MRI) scans at University College London Hospital NHS Foundation Trust patients. Through analysis of the images used in radiotherapy DeepMind Health will investigate improvements in efficiency of cancer treatment pathways.

Learning attribute models for applications like Zero-Shot Learning (ZSL) and image search is challenging because they require attribute classifiers to generalize to test data that may be very different from the training data. A typical scenario is when the notion of an attribute may differ from one user to another, e.g. one user may find a shoe formal whereas another user may not. In this case, the distribution of labels at test time is different from that at training time. We argue that due to the uncertainty in what the test distribution might be, committing to one attribute model during training is not advisable. We propose a novel framework for attribute learning which involves training an ensemble of diverse models for attributes and identifying experts from them at test time given a small amount of personalized annotations from a user. Our approach for attribute personalization is not specific to any classification model and we show results using Random Forest and SVM ensembles. We experiment with 2 datasets: SUN Attributes and Shoes and show significant improvements over baselines.