# Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI

arXiv: https://arxiv.org/abs/2006.12852 · HTML: https://mlsanity.com/paper/2006.12852/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 22 of 22)

- [Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images](https://mlsanity.com/paper/2103.03423/) (2021)
- [Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI](https://mlsanity.com/paper/2303.03758/) (2023)
- [Unsupervised Brain Anomaly Detection and Segmentation with Transformers](https://mlsanity.com/paper/2102.11650/) (2021)
- [Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images](https://mlsanity.com/paper/2109.01303/) (2021)
- [The role of noise in denoising models for anomaly detection in medical images](https://mlsanity.com/paper/2301.08330/) (2023)
- [Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs](https://mlsanity.com/paper/2312.04215/) (2023)
- [Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays](https://mlsanity.com/paper/2206.03935/) (2022)
- [Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder](https://mlsanity.com/paper/2203.11725/) (2022)
- [Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation](https://mlsanity.com/paper/2103.09094/) (2021)
- [On the Pitfalls of Using the Residual Error as Anomaly Score](https://mlsanity.com/paper/2202.03826/) (2022)
- [Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images](https://mlsanity.com/paper/2307.07534/) (2023)
- [Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images](https://mlsanity.com/paper/2207.02059/) (2022)
- [StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder](https://mlsanity.com/paper/2201.13271/) (2022)
- [Outlier-based Autism Detection using Longitudinal Structural MRI](https://mlsanity.com/paper/2202.09988/) (2022)
- [Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation](https://mlsanity.com/paper/2104.13786/) (2021)
- [Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensembles](https://mlsanity.com/paper/2412.17586/) (2024)
- [AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2305.12358/) (2023)
- [Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis](https://mlsanity.com/paper/2203.15347/) (2022)
- [nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods](https://mlsanity.com/paper/2209.01124/) (2022)
- [Transformer-based normative modelling for anomaly detection of early schizophrenia](https://mlsanity.com/paper/2212.04984/) (2022)
- [Automated triaging of head MRI examinations using convolutional neural networks](https://mlsanity.com/paper/2106.08176/) (2021)
- [A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRI](https://mlsanity.com/paper/2211.15527/) (2022)

## Abstract (arXiv)

Brain pathologies can vary greatly in size and shape, ranging from few pixels (i.e. MS lesions) to large, space-occupying tumors. Recently proposed Autoencoder-based methods for unsupervised anomaly segmentation in brain MRI have shown promising performance, but face difficulties in modeling distributions with high fidelity, which is crucial for accurate delineation of particularly small lesions. Here, similar to these previous works, we model the distribution of healthy brain MRI to localize pathologies from erroneous reconstructions. However, to achieve improved reconstruction fidelity at higher resolutions, we learn to compress and reconstruct different frequency bands of healthy brain MRI using the laplacian pyramid. In a range of experiments comparing our method to different State-of-the-Art approaches on three different brain MR datasets with MS lesions and tumors, we show improved anomaly segmentation performance and the general capability to obtain much more crisp reconstructions of input data at native resolution. The modeling of the laplacian pyramid further enables the delineation and aggregation of lesions at multiple scales, which allows to effectively cope with different pathologies and lesion sizes using a single model.