  There is a growing interest in single-class modelling and out-of-distribution
detection as fully supervised machine learning models cannot reliably identify
classes not included in their training. The long tail of infinitely many
out-of-distribution classes in real-world scenarios, e.g., for screening,
triage, and quality control, means that it is often necessary to train
single-class models that represent an expected feature distribution, e.g., from
only strictly healthy volunteer data. Conventional supervised machine learning
would require the collection of datasets that contain enough samples of all
possible diseases in every imaging modality, which is not realistic.
Self-supervised learning methods with synthetic anomalies are currently amongst
the most promising approaches, alongside generative auto-encoders that analyse
the residual reconstruction error. However, all methods suffer from a lack of
structured validation, which makes calibration for deployment difficult and
dataset-dependant. Our method alleviates this by making use of multiple
visually-distinct synthetic anomaly learning tasks for both training and
validation. This enables more robust training and generalisation. With our
approach we can readily outperform state-of-the-art methods, which we
demonstrate on exemplars in brain MRI and chest X-rays. Code is available at
https://github.com/matt-baugh/many-tasks-make-light-work .
