Many Tasks Make Light Work: Learning to Localise Medical Anomalies from Multiple Synthetic Tasks

Baugh M, Tan J, Müller JP, Dombrowski M, Batten J, Kainz B (2023)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2023

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 14220 LNCS

Pages Range: 162-172

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Vancouver, BC, CAN

ISBN: 9783031439063

DOI: 10.1007/978-3-031-43907-0_16

Abstract

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.

Authors with CRIS profile

Matthew Moses George Baugh
Bernhard Kainz

Related research project(s)

Medical Image Analysis with Normative Machine Learning (ERC-CoG MIA-NORMAL) Sept. 1, 2023 - Sept. 30, 2028

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich CH Switzerland (CH)

How to cite

APA:

Baugh, M., Tan, J., Müller, J.P., Dombrowski, M., Batten, J., & Kainz, B. (2023). Many Tasks Make Light Work: Learning to Localise Medical Anomalies from Multiple Synthetic Tasks. In Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 162-172). Vancouver, BC, CAN: Springer Science and Business Media Deutschland GmbH.

MLA:

Baugh, Matthew, et al. "Many Tasks Make Light Work: Learning to Localise Medical Anomalies from Multiple Synthetic Tasks." Proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, BC, CAN Ed. Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor, Springer Science and Business Media Deutschland GmbH, 2023. 162-172.

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