# Towards frugal unsupervised detection of subtle abnormalities in medical imaging

arXiv: https://arxiv.org/abs/2309.02458 · HTML: https://mlsanity.com/paper/2309.02458/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 4 Sep 2023.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study](https://mlsanity.com/paper/2004.03271/) (2020)
- [Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation](https://mlsanity.com/paper/1603.03236/) (2016)
- [Non-asymptotic Analysis of Biased Stochastic Approximation Scheme](https://mlsanity.com/paper/1902.00629/) (2019)
- [Unsupervised Pathology Detection: A Deep Dive Into the State of the Art](https://mlsanity.com/paper/2303.00609/) (2023)
- [On the Global Convergence of (Fast) Incremental Expectation Maximization Methods](https://mlsanity.com/paper/1910.12521/) (2019)
- [Frugal Machine Learning](https://mlsanity.com/paper/2111.03731/) (2021)
- [Fast Incremental Expectation Maximization for finite-sum optimization: nonasymptotic convergence](https://mlsanity.com/paper/2012.14670/) (2020)
- [Properties of the Stochastic Approximation EM Algorithm with Mini-batch Sampling](https://mlsanity.com/paper/1907.09164/) (2019)
- [Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients](https://mlsanity.com/paper/2110.12707/) (2021)
- [Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients](https://mlsanity.com/paper/2302.13593/) (2023)
- [Online EM for Functional Data](https://mlsanity.com/paper/1604.00570/) (2016)
- [A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm](https://mlsanity.com/paper/2012.01929/) (2020)

## Abstract (arXiv)

Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. This problem can be addressed through unsupervised anomaly detection (UAD) methods, which identify features that do not match with a reference model of normal profiles. Artificial neural networks have been extensively used for UAD but they do not generally achieve an optimal trade-ohookleftarrow between accuracy and computational demand. As an alternative, we investigate mixtures of probability distributions whose versatility has been widely recognized for a variety of data and tasks, while not requiring excessive design ehookleftarrowort or tuning. Their expressivity makes them good candidates to account for complex multivariate reference models. Their much smaller number of parameters makes them more amenable to interpretation and e cient learning. However, standard estimation procedures, such as the Expectation-Maximization algorithm, do not scale well to large data volumes as they require high memory usage. To address this issue, we propose to incrementally compute inferential quantities. This online approach is illustrated on the challenging detection of subtle abnormalities in MR brain scans for the follow-up of newly diagnosed Parkinsonian patients. The identified structural abnormalities are consistent with the disease progression, as accounted by the Hoehn and Yahr scale.