Data collection, curation and annotation are critical components in machine learning for medical image analysis which can have profound effects on the overall performance, robustness and generalisation. In this talk, we will illustrate this problem with results from an empirical study using multi-site neuroimaging data. We will discuss mechanisms for assessing the readiness of clinical data for machine learning and we will consider methods for pre- and post-analysis quality control. We conclude by discussing how causal reasoning may help us to identify data issues early on, and find solutions to tackle the key challenges of domain shift and data scarcity.

0.5 CPD credit
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