# Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks

arXiv: https://arxiv.org/abs/2307.00899 · HTML: https://mlsanity.com/paper/2307.00899/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 3 Jul 2023.

## Builds on (most cited first, 18 of 18)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations](https://mlsanity.com/paper/2012.15029/) (2020)
- [Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study](https://mlsanity.com/paper/2004.03271/) (2020)
- [Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization](https://mlsanity.com/paper/2109.15222/) (2021)
- [Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models](https://mlsanity.com/paper/2206.03461/) (2022)
- [Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1812.05941/) (2018)
- [Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images](https://mlsanity.com/paper/2103.03423/) (2021)
- [Detecting Outliers with Foreign Patch Interpolation](https://mlsanity.com/paper/2011.04197/) (2020)
- [Detecting Outliers with Poisson Image Interpolation](https://mlsanity.com/paper/2107.02622/) (2021)
- [Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI](https://mlsanity.com/paper/2109.06023/) (2021)
- [The role of noise in denoising models for anomaly detection in medical images](https://mlsanity.com/paper/2301.08330/) (2023)
- [Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays](https://mlsanity.com/paper/2206.03935/) (2022)
- [On the Pitfalls of Using the Residual Error as Anomaly Score](https://mlsanity.com/paper/2202.03826/) (2022)
- [CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization](https://mlsanity.com/paper/2301.02126/) (2023)

## Built on by (most cited first, 12 of 12)

- [MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection](https://mlsanity.com/paper/2405.11315/) (2024)
- [MedIAnomaly: A comparative study of anomaly detection in medical images](https://mlsanity.com/paper/2404.04518/) (2024)
- [Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2407.06635/) (2024)
- [CF-Seg: Counterfactuals meet Segmentation](https://mlsanity.com/paper/2506.16213/) (2025)
- [Is Hyperbolic Space All You Need for Medical Anomaly Detection?](https://mlsanity.com/paper/2505.21228/) (2025)
- [Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensembles](https://mlsanity.com/paper/2412.17586/) (2024)
- [Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays](https://mlsanity.com/paper/2405.11976/) (2024)
- [A Principled Approach to Unsupervised Anomaly Detection](https://mlsanity.com/paper/2609.21800/) (2026)
- [Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review](https://mlsanity.com/paper/2510.14462/) (2025)
- [Unsupervised Anomaly Detection of Diseases in the Female Pelvis for Real-Time MR Imaging](https://mlsanity.com/paper/2602.06179/) (2026)
- [Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging](https://mlsanity.com/paper/2605.05161/) (2026)
- [A Data-Interventional Framework for Auditing Privacy and Fairness in Generative Medical Imaging](https://mlsanity.com/paper/2609.26623/) (2026)

## Abstract (arXiv)

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 .