# Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review

Youwan Mahé, Elise Bannier, Stéphanie Leplaideur, Elisa Fromont, Francesca Galassi

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Youwan Mahé, Elise Bannier, Stéphanie Leplaideur, Elisa Fromont, Francesca Galassi. Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review. 2025. [hal-05304920](https://hal-05304920)

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# Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review

Youwan Mahé,$^{1,4*}$, Elise Bannier$^{1,3}$, Stéphanie Leplaideur$^{1,2,6}$, Elisa Fromont$^{5§}$ and Francesca Galassi,$^{1*§}$

$^{1}$Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn, Rennes, France,

$^{2}$CHU Rennes, Physical Medicine and Rehabilitation Department, Rennes, France

$^{3}$CHU Rennes, Radiology Department, Rennes, France

$^{4}$Siemens Healthineers, Courbevoie, France

$^{5}$Univ Rennes, Inria, CNRS, IRISA, Rennes, France

$^{6}$Centre de Kerpape, Ploemeur, France

$^{§}$These authors contributed equally as co-last authors.

*Correspondence: [youwan.mahe, francesca.galassi]@inria.fr

October 8, 2025

## Abstract:

Unsupervised deep generative models are emerging as a promising alternative to supervised methods for detecting and segmenting anomalies in brain imaging. Unlike fully supervised approaches, which require large voxel-level annotated datasets and are limited to well-characterised pathologies, these models can be trained exclusively on healthy data and identify anomalies as deviations from learned normative brain structures. This PRISMA-ScR-guided scoping review synthesises recent work on unsupervised deep generative models for anomaly detection in neuroimaging, including autoencoders, variational autoencoders, generative adversarial networks, and denoising diffusion models. A total of 49 studies published between 2018 and 2025 were identified, covering applications to brain MRI and, less frequently, CT across diverse pathologies such as tumours, stroke, multiple sclerosis, and small vessel disease. Reported performance metrics (Dice, AUROC, AUPRC) are compared
alongside architectural design choices such as dimensionality, masking, patching, and loss formulations. Across the included studies, generative models achieved encouraging performance for large focal lesions and demonstrated steady progress in addressing more subtle and heterogeneous abnormalities. While supervised methods remain the benchmark, unsupervised approaches are advancing rapidly, with increasing adoption of 3D architectures and anatomy-aware designs. A key strength of generative models is their ability to produce interpretable pseudo-healthy (also referred to as counterfactual) reconstructions, which is particularly valuable when annotated data are scarce, as in rare or heterogeneous diseases. Looking ahead, these models offer a compelling direction for anomaly detection, enabling semi-supervised learning, supporting the discovery of novel imaging biomarkers, and facilitating within- and cross-disease deviation mapping in unified end-to-end frameworks. To realise clinical impact, future work should prioritise anatomy-aware modelling, development of foundation models, task-appropriate evaluation metrics, and rigorous clinical validation.

**Keywords:** Unsupervised Anomaly Detection (UAD), Deep Generative Modelling, Neuroimaging, Magnetic Resonance Imaging (MRI)

# 1 Introduction

Advances in brain imaging have markedly improved the diagnosis, monitoring, and prognosis of neurological disease. In clinical practice, magnetic resonance imaging (MRI) enables non-invasive, high-resolution characterisation of brain structure and supports *in vivo* identification of anomalies in conditions such as gliomas, ischaemic stroke, multiple sclerosis (MS), and some neurodegenerative disorders. These abnormalities include large focal masses, vascular infarcts, sparse white-matter hyperintensities, and rare malformations of cortical development (Severino et al., 2020). Their appearance varies substantially across MRI sequences and acquisition protocols (Vemuri et al., 2022; Villanueva-Meyer et al., 2017).

High-quality segmentation - the accurate and reproducible delineation of pathological and anatomical structures - is essential for deriving quantitative imaging biomarkers such as lesion load, spatial distribution, and volumetric change over time. These biomarkers support objective assessment and longitudinal follow-up in both clinical and research contexts. In the absence of validated automated tools, manual detection and segmentation by expert radiologists remain the reference standard. However, these procedures are time-consuming, require specialised expertise, and are subject to inter- and intra-rater variability (Walsh et al., 2023), which can compromise accuracy and reproducibility, especially in large-scale or multicentre studies (García-Lorenzo et al., 2013).
Automated approaches were introduced to address these limitations. Early methods relied on classical image processing such as edge detection, region growing, and morphological filtering and statistical modelling techniques, including Gaussian mixture models, fuzzy c-means clustering, and Markov random fields (Gonzalez & Woods, 2007; Pham et al., 2000). While effective in controlled settings, these approaches relied on expert-defined features (e.g., intensity, texture, atlas-derived priors) and often degraded in the presence of anatomical variability, heterogeneous lesion characteristics, and site/protocol differences (Commowick et al., 2018; Xu et al., 2024).

Deep learning has transformed medical image analysis by learning multi-scale features directly from data (Chan et al., 2020; Litjens et al., 2017; Lladó et al., 2012; Shen et al., 2017). In neuroimaging, convolutional architectures such as U-Net and its derivatives, including nnU-Net, are widely adopted for supervised lesion detection and segmentation (Isensee et al., 2024; Ronneberger et al., 2015). When trained and evaluated on datasets with closely matched characteristics, these models consistently achieve state-of-the-art performance. However, their accuracy often deteriorates under distribution shifts caused by differences in pathology subtype, patient characteristics, or acquisition protocol (Ackaouy et al., 2020; Ghafoorian et al., 2017). Moreover, they require large, voxel-level annotated datasets, such as BraTS for brain tumours (Menze et al., 2015) and MSSEG for multiple sclerosis (Commowick et al., 2021), which are costly to obtain and especially scarce in rare or heterogeneous diseases (Lee et al., 2022). Furthermore, because these annotations encode predefined lesion categories, such models are inherently constrained to known biomarkers and may overlook novel or subtle imaging signatures (Gill et al., 2023).

In contrast, unsupervised anomaly detection (UAD) methods learn from unannotated healthy data, enabling identification of pathological regions without voxel-level labels. This approach is particularly suited to rare diseases and heterogeneous conditions, where high-quality annotations are scarce or infeasible. To stress-test generalisation under open-set conditions, Bercea et al. introduced NOVA, an evaluation-only benchmark of ~900 brain MRI scans spanning 281 rare diagnoses (Bercea, Li, et al., 2025). NOVA highlighted substantial performance drops for state-of-the-art vision-language models in anomaly localisation, captioning, and diagnostic reasoning, underscoring the need for pathology-agnostic generative approaches. One of the most widely adopted strategies in this direction is *pseudo-healthy reconstruction*, where a generative model trained on healthy brain images synthesises a subject-specific *healthy* counterpart of the input. Comparing the original scan with this counterfactual reconstruction highlights deviations from normal anatomy, thereby enabling detection of abnormalities - including rare ones - without requiring voxel-level annotations. Early implementations used autoencoders and variational autoencoders (VAEs), which learn compact latent representations of healthy anatomy (Zimmerer et al., 2019). Subsequent work employed generative adversarial networks (GANs) to improve reconstruction realism and sharpen lesion boundaries (Schlegl et al., 2019). These developments have been summarised
in targeted reviews, including Baur, Denner, et al. (2021) on autoencoder-based brain MRI anomaly
detection, Wang et al. (2023) on GAN-based methods in neuroimaging, as well as in broader surveys
of generative approaches for medical image analysis such as Pang et al. (2021) and Tschuchnig and
Gadermayr (2022). More recently, denoising diffusion probabilistic models (DDPMs) (Ho et al., 2020) -
originally developed for natural image synthesis - have been adapted for neuroimaging, showing strong
capability for modelling complex anatomical variability (Bercea et al., 2023; Pinaya, Tudosiu, et al.,
2022). Kazerouni et al. (2023) reviewed their applications in medical imaging, from pseudo-healthy
reconstruction to conditional synthesis and segmentation. Beyond diffusion, continuous-time generative
frameworks such as flow matching (Lipman et al., 2023) have emerged as deterministic alternatives,
showing early promise in accelerated MRI reconstruction and high-fidelity volumetric image generation
(Yazdani et al., 2025; Zhao et al., 2025). A description of the generative modelling framework for
unsupervised anomaly detection, including its underlying principles and pseudo-healthy reconstruction
strategy, is provided in Subsection 2.1.

To our knowledge, this is the first systematic scoping review focused specifically on unsupervised deep generative models for anomaly detection in neuroimaging, covering the evolution from autoencoders and GANs to the latest diffusion-based methods and emerging continuous-time approaches such as flow matching. Unlike prior surveys, we not only summarise model architectures and training strategies but also compare performance using both segmentation (Dice) and classification/detection metrics (AUROC, AUPRC), disaggregated by pathology type. This pathology-specific perspective, combined with an analysis of dataset usage and dimensionality (2D vs. 3D), provides a clinically relevant assessment of methods and highlights gaps, such as the unexplored potential of flow matching for brain anomaly detection, that offer promising directions for future research.

## 1.1 Review questions

This review seeks to answer the following questions :

* How have unsupervised deep generative models been applied to anomaly detection and segmentation in brain MRI over the past seven years?

* What performance levels do these methods achieve across different pathologies (e.g., tumours, stroke, multiple sclerosis, white matter hyperintensities) ?

* Which design choices (e.g., dimensionality, patching, masking, loss functions, pre-training) most strongly influence performance ?
* What emerging paradigms appear most promising for overcoming current bottlenecks ?

# 2 Methods

## 2.1 Background: generative modelling for UDA

Generative models aim to approximate the underlying data distribution $p_{\text{data}}(\mathbf{x})$ by learning a parametric model $p_{\theta}(\mathbf{x})$ from a representative training dataset. A common formulation introduces a latent variable $\mathbf{z} \in \mathbb{R}^d$ drawn from a prior distribution $p(\mathbf{z})$, typically a standard multivariate Gaussian, and a neural network $G_{\theta}$ (decoder or generator) that maps latent codes to the data space:

$$ \mathbf{z} \sim p(\mathbf{z}) = \mathcal{N}(\mathbf{0}, \mathbf{I}), \quad \mathbf{x} = G_{\theta}(\mathbf{z}), \quad \mathbf{x} \sim p_{\theta}(\mathbf{x}). \qquad (1) $$

The model parameters $\theta$ are estimated by maximising the data log-likelihood (or an approximation when exact maximisation is intractable):

$$ \max_{\theta} \mathbb{E}_{\mathbf{x} \sim p_{\text{data}}} [\log p_{\theta}(\mathbf{x})]. \qquad (2) $$

In unsupervised anomaly detection, $p_{\text{data}}$ contains only healthy brain images, so the model learns a normative distribution of healthy anatomy. At inference, a test image $\mathbf{x}_{\text{test}}$ is passed through the trained model to produce a *pseudo-healthy* or *counterfactual* reconstruction $\hat{\mathbf{x}} = G_{\theta}(\mathbf{x}_{\text{test}})$ that represents the same subject but without pathology. Because pathological patterns are not part of the learned distribution, they are typically not reproduced in $\hat{\mathbf{x}}$. A residual map

$$ \mathbf{r} = \mathbf{x}_{\text{test}} - \hat{\mathbf{x}} \qquad (3) $$

then highlights voxels that deviate from the healthy distribution, providing localised anomaly maps without requiring voxel-level annotations. This counterfactual reconstruction principle underpins most unsupervised approaches reviewed here.
## 2.2 Information sources and search strategy

This review was conducted in accordance with the PRISMA-ScR (2018) guidelines for scoping reviews (Tricco et al., 2018). We searched PubMed, Web of Science, ScienceDirect, Springer Link, IEEE Xplore, and ArXiv up to 8 September 2025. Boolean queries combined terms related to *unsupervised anomaly detection, neuroimaging* (MRI or CT), and *deep learning*, as summarised in Table 1. Reference lists of relevant articles and reviews were also screened. Searches were limited to articles published in English.

<table><caption>Table 1: Boolean queries used for database searching</caption><thead><tr><td>Database</td><td>Query</td><td>Date</td></tr></thead><tbody><tr><td>PubMed</td><td>(Anomaly AND Unsupervised) AND (Brain) AND (MRI OR CT) AND (Machine Learning OR Deep Learning)</td><td>Sep 08 2025</td></tr><tr><td>Web Of Science</td><td>((TS=Anomaly) AND (TS=unsupervised) AND (TS=brain)) AND ((TS=MRI) OR (TS=CT)) AND ((TS=Machine Learning) OR (TS=Deep Learning))</td><td>Sep 08 2025</td></tr><tr><td>Science Direct</td><td>Unsupervised Anomaly Brain MRI CT Machine Learning Filter: Research Article</td><td>Sep 08 2025</td></tr><tr><td>ArXiv</td><td>Unsupervised AND Anomaly AND Brain AND "Deep learning", in Computer Science (cs)</td><td>Sep 08 2025</td></tr><tr><td>IEEE Xplore</td><td>Unsupervised AND Anomaly AND Brain AND "Deep Learning"</td><td>Sep 08 2025</td></tr><tr><td>Springer Nature Link</td><td>(Anomaly AND Unsupervised AND Brain) AND (MRI OR CT) AND (Machine Learning OR Deep Learning) AND ("Conference Paper" OR "Research Article") AND ("Computer Vision" OR "Machine Learning")</td><td>Sep 08 2025</td></tr></tbody></table>

## 2.3 Eligibility and screening

Studies were eligible if they:

* applied unsupervised or generative deep learning methods for anomaly detection or segmentation in neuroimaging (MRI or CT);

* reported at least one quantitative evaluation metric relevant to detection (e.g., AUROC, AUPRC) or segmentation (e.g., Dice);
* used real human imaging data from public datasets or institutional cohorts.

We excluded studies that employed rule-based or non-deep learning methods, supervised or semi-supervised approaches, non-neuroimaging applications, animal or synthetic-only data, review or survey papers, and non-research formats (e.g., abstracts, editorials).

Search results were imported into Rayyan (Ouzzani et al., 2016) for duplicate removal and blinded screening by two reviewers. Titles and abstracts were screened first, followed by full-text assessment of potentially relevant studies. Disagreements were resolved through discussion until consensus was reached.

Data extraction was carried out using the export functions of each database. Retrieved publications were saved as CSV or BibTeX files and subsequently imported into Rayyan, which automatically matched and retrieved the corresponding records. For arXiv, where no export function is available through the web interface, we used the API via custom Python scripts to obtain the publication data. Full-text articles were accessed through institutional subscriptions.

## 2.4 Risk of bias assessment

Risk of bias was assessed with a focus on methodological quality. Guided by PRISMA recommendations for systematic reviews (Page et al., 2021), two reviewers (YM, FG) independently screened and appraised all records in Rayyan (Ouzzani et al., 2016) using blind mode. Disagreements were resolved by discussion until consensus. As no validated risk-of-bias tool exists for anomaly detection in medical imaging, we applied a structured checklist covering dataset characteristics (e.g., public availability, diversity of pathologies), as well as reproducibility and transparency (e.g., code and data availability). For studies raising concerns about reporting integrity, we additionally screened them with the *Problematic Paper Screener* (Cabanac et al., 2022) and documented outcomes.

# 3 Results

## 3.1 Study selection

The initial search yielded 536 records, which were reduced to 479 after deduplication. Following screening, 418 records were excluded for specific reasons, including being out of scope, not involving neuroimaging, not using deep learning, employing rule-based approaches, or being survey articles. A total of 61 full texts
were assessed for eligibility, resulting in 49 reports remaining after excluding retracted, unretrievable, and non-research articles (Fig. 1). The selected studies span the period 2018–2025 and provide a 7-year overview of unsupervised generative models for neuroimaging anomaly detection.

<figure><img src="image_2.png" /><figcaption>Figure 1: PRISMA flow diagram for scoping reviews, including database and register searches.</figcaption></figure>

## 3.2 Study characteristics

Across the 49 studies, we observed four main families of unsupervised generative approaches for anomaly detection in neuroimaging: autoencoders (including denoising and attention variants), variational autoencoders (including VQ-VAEs and context-encoding hybrids), generative adversarial networks (f-AnoGAN-style and cycle/symmetry-augmented), and diffusion models (pixel-space DDPMs, latent diffusion, and masked/patch variants). A small number of papers used non-generative but closely related self-supervised/discriminative approaches (e.g., synthetic lesion pretext tasks, normalising flows); we summarise these separately for completeness.

MRI constituted the primary imaging modality (T1-w, T1c, T2-w, FLAIR), with occasional use of diffusion tensor imaging (DTI) and, less frequently, computed tomography (CT) and positron emission tomography (FDG-PET). Most methods processed 2D slices, although an increasing subset employed 3D architectures, particularly recent AE/VAE and a few GAN/diffusion studies.

## 3.3 Evaluation metrics

Most studies reported either segmentation or detection performance. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC), defined as

$$DSC = \frac{2|X \cap Y|}{|X| + |Y|} \qquad (4)$$
<figure><img src="image_3.png" /><figcaption>Figure 2: Central axial slices from a healthy brain (IXI), a brain tumour case (BraTS), a chronic stroke case (ATLAS v2.0), and a multiple sclerosis case (MSSEG).</figcaption></figure>

where *X* is the predicted segmentation and *Y* the reference annotation. Detection, framed as a binary classification task (pathological vs. non-pathological), was most often quantified using the area under the receiver operating characteristic curve (AUROC), and the area under the precision-recall curve (AUPRC), the latter being more informative under class imbalance where anomalies are rare.

**3.4 Datasets and pathologies**

Performance metrics were applied across a range of publicly available and institutional datasets. Three pathology groups dominated: brain tumours, multiple sclerosis/white-matter hyperintensities, and stroke. Fewer studies targeted neurodegenerative conditions (e.g., Alzheimer's disease, Parkinson's disease), neonatal encephalopathy, or healthy ageing. Representative examples of central axial slices from the main pathological datasets are shown in Figure 2.

**Brain tumours.** The vast majority of tumour studies relied on the BraTS dataset (Menze et al., 2015), which provides multi-sequence MRI including T1-w, contrast-enhanced T1 (T1c), T2-w, and FLAIR images. Some other used in-house datasets, or the *neuroimaging dataset of brain tumour patient* from Pernet et al. (2016). Gliomas, the primary pathology represented in BraTS, typically produce some of the largest lesions observed in neuroimaging, comparable in size to stroke. Lesion morphology varies substantially with tumour grade, and growth within the cranial cavity frequently distorts surrounding anatomical structures. Sequence choice is clinically motivated: T2-w and FLAIR highlight water content and oedema, while T1c reveals intratumoral activity through contrast uptake (Menze et al., 2015).
**Stroke.** Stroke lesions were primarily represented by two datasets: ISLES (Ischaemic Stroke Lesion Segmentation) (Hernandez Petzsche et al., 2022) and ATLAS (versions 1.2 and 2.0) (Liew et al., 2021). ISLES focuses on acute and sub-acute ischaemic strokes, providing diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and FLAIR sequences for approximately 400 cases. In the acute phase, DWI and FLAIR show marked hyperintensity in affected regions, whereas in the subacute-to-chronic phase, the DWI signal diminishes, making lesion delineation on FLAIR alone more challenging (Hernandez Petzsche et al., 2022). ATLAS, by contrast, comprises 955 cases of chronic stroke with high-resolution T1-w MRI. Although valuable for large-scale research, chronic lesions are often subtler and more heterogeneous, rendering consistent delineation more difficult (Liew et al., 2021).

**Multiple sclerosis and white-matter hyperintensities.** Multiple sclerosis and white-matter hyperintensities were evaluated using datasets such as MSSEG (Commowick et al., 2021), MSLUB (Lesjak et al., 2017) and the WMH Challenge cohort (Kuijf et al., 2022). In contrast to tumours or stroke, MS and WMH lesions are typically small and sparse, making them particularly challenging for unsupervised detection. Lesions are most conspicuous on FLAIR and T2-w MRI, where they appear as hyperintense regions within the white matter. These characteristics contribute to substantial variability in segmentation performance across studies and remain a major bottleneck for anomaly detection methods.

**Neurodegeneration and other conditions.** A smaller number of studies targeted neurodegenerative disorders, most often Alzheimer's disease using the Alzheimer's Disease Neuroimaging Initiative (ADNI) (Beckett et al., 2015), a large public repository containing clinical and neuroimaging data from healthy individuals and pathological subjects with varying stages of Alzheimer's disease. Unlike tumours or stroke, Alzheimer's disease does not typically produce focal lesions visible on structural MRI. Instead, group-level analyses such as voxel-based morphometry reveal localised patterns of atrophy, particularly in regions associated with cognitive decline (Varghese et al., 2013). A single study applied diffusion MRI to the Parkinson's Progression Markers Initiative (PPMI) dataset (Marek et al., 2018), which contains diffusion tensor images of newly diagnosed Parkinson's disease patients. Similarly to Alzheimer's disease, Parkinson's disease does not produce a single focal lesion but instead leads to subtle, distributed changes in brain structure (Péran et al., 2010). Other studies have leveraged normal ageing images from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset (Shafto et al., 2014) and data from the Medical Out-of-Distribution (MOOD) challenge (Zimmerer et al., 2020), while neonatal anomalies were studied using images from the Developing Human Connectome Project (dHCP) (Hughes et al., 2017). Traumatic brain injury is represented by subjects from the Center-TBI dataset (Steyerberg et al., 2019), which features lesions captured via computed tomography.
**Healthy Control Datasets.** In addition to pathological cohorts, many studies relied on healthy control datasets to model normative brain anatomy. Commonly used resources included OASIS-3 (LaMontagne et al., 2019), a longitudinal collection of clinical and neuroimaging data from cognitively normal adults and individuals at risk of dementia; the IXI dataset,<fnref n="1" /> comprising structural MRI from healthy volunteers across three London hospitals; the cognitively normal subset of ADNI (Beckett et al., 2015); and participants of the Neurofeedback Skull-stripped (NFBS) (Puccio et al., 2016) repository. These datasets provided representative samples of healthy anatomy across a wide age range and imaging protocols, forming the basis for training generative models to detect deviations associated with pathology.

<footnote n="1">¹http://brain-development.org/ixi-dataset/</footnote>
<table><caption>Table 2: Comparison of unsupervised anomaly detection methods included in the review. MS: Multiple Sclerosis, TBI: Traumatic Brain Injury and WMH: White Matter Hyperintensities. * denotes the presence of a working link to a github repository containing the code for the presented method. **Bold** denotes the best performance across pathologies for a specific method, *Underline* denotes the best performance across method for a specific pathology (excluding self-supervised methods and synthetic lesions).</caption><thead><tr><th>Reference</th><th>Pathology (Dataset)</th><th>Modality</th><th>Dim.</th><th>Dice</th><th>AUROC</th><th>AUPRC</th><th>Keyword</th></tr></thead><tbody><tr><td colspan="8"><strong>Autoencoders</strong></td></tr><tr><td>Baur et al., 2018</td><td>MS (In-house)</td><td>MRI (T1, FLAIR)</td><td>2D</td><td>0.605</td><td>-</td><td>-</td><td></td></tr><tr><td>Baur, Wiestler, et al., 2021</td><td>Tumour (In-house)</td><td>MRI (FLAIR)</td><td>3D</td><td>0.390</td><td>-</td><td>0.300</td><td></td></tr><tr><td>Behrendt et al., 2022</td><td>Tumour (BraTS)</td><td>MRI (T1)</td><td>3D</td><td>-</td><td>0.935</td><td>-</td><td>Dataset Impurities</td></tr><tr><td>Muñoz-Ramírez et al., 2022</td><td>Parkinson (PPMI<sup>2</sup>)</td><td>MRI (DTI)</td><td>2D</td><td>-</td><td>0.682</td><td>-</td><td>DTI</td></tr><tr><td>Ghorbel et al., 2023</td><td>Tumour (BraTS)</td><td>MRI (FLAIR)</td><td>2D</td><td>0.502</td><td>0.780</td><td>0.425</td><td>Transformer</td></tr><tr><td></td><td>MS (MSLUB)</td><td>MRI (FLAIR)</td><td>2D</td><td>0.173</td><td><u>0.886</u></td><td>0.203</td><td></td></tr><tr><td>Kascenas et al., 2023 *</td><td>Tumour (BraTS)</td><td>MRI (T1, T1c, T2, FLAIR)</td><td>2D</td><td><u>0.773</u></td><td>-</td><td><u>0.833</u></td><td>Denoising AE</td></tr><tr><td></td><td>MS (In-house)</td><td>MRI (FLAIR)</td><td>3D</td><td><u>0.650</u></td><td>-</td><td>0.670</td><td></td></tr><tr><td></td><td>WMH (WMH)</td><td>MRI (T1, FLAIR)</td><td>3D</td><td>0.450</td><td>-</td><td>0.370</td><td></td></tr><tr><td>Luo et al., 2023 *</td><td>Tumour (BraTS)</td><td>MRI (T2)</td><td>3D</td><td>0.462</td><td>0.844</td><td>0.741</td><td>3D AE</td></tr><tr><td></td><td>Stroke (In-house)</td><td>MRI (T2)</td><td>3D</td><td>-</td><td><u>0.807</u></td><td><u>0.705</u></td><td></td></tr><tr><td></td><td>MS (In-house)</td><td>MRI (T2)</td><td>3D</td><td>-</td><td>0.858</td><td><u>0.731</u></td><td></td></tr><tr><td>Meissen et al., 2023 *</td><td>Tumour (BraTS)</td><td>MRI (T1)</td><td>2D</td><td>0.400</td><td>0.770</td><td>-</td><td>2-stage</td></tr></tbody></table>

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<footnote n="2">²Marek et al., 2018</footnote>
<table><thead><tr><th>Reference</th><th>Pathology (Dataset)</th><th>Modality</th><th>Dim.</th><th>Dice</th><th>AUROC</th><th>AUPRC</th><th>Keyword</th></tr></thead><tbody><tr><td>Avci et al., 2024 *</td><td>Aging (Cam-CAN<fnref n="3" />, MOOD<fnref n="4" />)</td><td>MRI (T1)</td><td>2D</td><td>0.336</td><td>0.775</td><td>-</td><td></td></tr><tr><td>Jiménez-García et al., 2024</td><td>Alzheimer (ADNI)</td><td>MRI (T1)</td><td>3D</td><td>-</td><td>0.800</td><td>-</td><td>deformable AE</td></tr><tr><td>Lu et al., 2024</td><td>Tumour (BraTS)</td><td>T1, T1c, T2, FLAIR</td><td>3D</td><td>0.471</td><td>0.838</td><td>-</td><td>Elastic transform</td></tr><tr><td></td><td>Tumour (In-house)</td><td>MRI (T1)</td><td>2D</td><td>-</td><td><strong><u>0.992</u></strong></td><td>-</td><td>3D AE</td></tr><tr><td colspan="8"><strong>Variational autoencoders</strong></td></tr><tr><td>Sato et al., 2019</td><td>Tumour (BraTS)</td><td>MRI (T1, T2)</td><td>3D</td><td>-</td><td>0.582</td><td>-</td><td>Tailored loss</td></tr><tr><td>Uzunova et al., 2019</td><td>Stroke (ATLAS)</td><td>MRI (T1)</td><td>3D</td><td>-</td><td>0.672</td><td>-</td><td></td></tr><tr><td>Zimmerer et al., 2019 *</td><td>Tumour (BraTS)</td><td>MRI (T1c, T2, FLAIR)</td><td>3D</td><td>0.500</td><td>0.940</td><td>-</td><td>Conditional</td></tr><tr><td>Bengs et al., 2021</td><td>Tumour (BraTS)</td><td>MRI (T1, T1c, T2, FLAIR)</td><td>2D</td><td>0.440</td><td>0.820</td><td>-</td><td>Tailored Loss</td></tr><tr><td>Lambert et al., 2021</td><td>Stroke (ATLAS)</td><td>MRI (T1)</td><td>3D</td><td>0.302</td><td>-</td><td>0.279</td><td>3D VAE</td></tr><tr><td>Chatterjee et al., 2022 *</td><td>Tumour (BraTS)</td><td>MRI (T1, T1c, T2, FLAIR)</td><td>3D</td><td><u>0.331</u></td><td>-</td><td>0.256</td><td></td></tr><tr><td>Pinaya et al., 2022</td><td>MS (MSLUB)</td><td>MRI (FLAIR)</td><td>3D</td><td><strong><u>0.650</u></strong></td><td>-</td><td>-</td><td>3D VAE</td></tr><tr><td></td><td>Tumour (BraTS)</td><td>MRI (FLAIR)</td><td>3D</td><td>0.463</td><td>-</td><td>-</td><td></td></tr><tr><td></td><td></td><td>MRI (T1, T2)</td><td>2D</td><td>0.531</td><td>-</td><td>-</td><td>Context Encoding</td></tr><tr><td></td><td>Synthetic lesions</td><td>MRI</td><td>2D</td><td>0.723</td><td>-</td><td>-</td><td></td></tr><tr><td></td><td>MS (MSLUB)</td><td>MRI (FLAIR)</td><td>2D</td><td>0.378</td><td>-</td><td>0.272</td><td>Transformer</td></tr><tr><td></td><td>Tumour (BraTS)</td><td>MRI (FLAIR)</td><td>2D</td><td>0.537</td><td>-</td><td>0.555</td><td></td></tr></tbody></table>

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<footnote n="3">³Shafto et al., 2014</footnote>

<footnote n="4">⁴Zimmerer et al., 2020</footnote>
<table><thead><tr><th>Reference</th><th>Pathology (Dataset)</th><th>Modality</th><th>Dim.</th><th>Dice</th><th>AUROC</th><th>AUPRC</th><th>Keyword</th></tr></thead><tbody><tr><td>Lüth et al., 2023</td><td>WMH (WMH)<br>Tumour (BraTS)<br>Stroke (ISLES)</td><td>MRI (FLAIR)<br>MRI<br>(T1, T1c, T2, FLAIR)<br>MRI (FLAIR)</td><td>2D<br>2D<br>2D</td><td><u>0.429</u><br>–<br>–</td><td>–<br>0.826<br>0.693</td><td><u>0.320</u><br><b>0.819</b><br>0.549</td><td>Contrastive</td></tr><tr><td>Raad et al., 2023 *</td><td>Neonatal anomalies (dHCP<sup>5</sup>)</td><td>MRI (T2)</td><td>3D</td><td>–</td><td>0.830</td><td>–</td><td>Rare disease</td></tr><tr><td>Solal et al., 2023</td><td>Alzheimer (ADNI)</td><td>PET</td><td>3D</td><td>–</td><td>–</td><td>–</td><td>PET-scan</td></tr><tr><td>Hassanaly et al., 2024 *</td><td>Alzheimer (In-house)</td><td>PET</td><td>3D</td><td>–</td><td>–</td><td>–</td><td>PET-scan</td></tr><tr><td>Huijben et al., 2024 *</td><td>Synthetic lesions</td><td>MRI (T1)</td><td>2D</td><td>–</td><td>–</td><td>0.660</td><td>Synthetic lesions</td></tr><tr><td>Wijanarko et al., 2024</td><td>Tumour (BraTS)</td><td>MRI<br>(T1, T1c, T2, FLAIR)</td><td>2D</td><td>0.606</td><td><b>0.968</b></td><td>0.462</td><td>Tailored loss</td></tr><tr><td colspan="8"><strong>Generative adversarial networks</strong></td></tr><tr><td>Schlegl et al., 2019 *</td><td>Macular edema (In-house)</td><td>OCT</td><td>2D</td><td>–</td><td>0.783</td><td>–</td><td>Wassertstein</td></tr><tr><td>Simarro et al., 2020</td><td>TBI (Center-TBI<sup>6</sup>)</td><td>CT</td><td>3D</td><td>–</td><td>0.750</td><td>–</td><td>Wassertstein</td></tr><tr><td>Dey et al., 2021</td><td>Tumour (BraTS)<br>MS (MSSEG)</td><td>MRI (T2, FLAIR)<br>MRI (FLAIR)</td><td>2D<br>2D</td><td>0.680<br>0.482</td><td>–<br>–</td><td>–</td><td></td></tr><tr><td>Nguyen et al., 2021</td><td>Tumour (BraTS)</td><td>MRI (T1)</td><td>2D</td><td><b>0.770</b></td><td>–</td><td>–</td><td>2-stage</td></tr><tr><td>Wu et al., 2021</td><td>Tumour (BraTS)</td><td>MRI<br>(T1, T1c, T2, FLAIR)</td><td>3D</td><td>0.619</td><td>–</td><td>–</td><td>Symmetric</td></tr><tr><td>Cabreza et al., 2022</td><td>Alzheimer (OASIS-3)</td><td>MRI (T1)</td><td>2D</td><td>–</td><td>0.795</td><td>–</td><td></td></tr><tr><td>Rahman Siddiquee et al., 2024 *</td><td>Alzheimer (ADNI)</td><td>MRI (T1)</td><td>2D</td><td>–</td><td>0.655</td><td>–</td><td>Patch GAN</td></tr></tbody></table>

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<footnote n="5">⁵Hughes et al., 2017</footnote>

<footnote n="6">⁶Steyerberg et al., 2019</footnote>
<table><thead><tr><th>Reference</th><th>Pathology (Dataset)</th><th>Modality</th><th>Dim.</th><th>Dice</th><th>AUROC</th><th>AUPRC</th><th>Keyword</th></tr></thead><tbody><tr><td>Bougaham et al., 2025 *</td><td>Tumour (BraTS)</td><td>MRI (T1, T1c, T2, FLAIR)</td><td>2D</td><td>-</td><td><strong>0.932</strong></td><td>-</td><td>Cycle GAN</td></tr><tr><td colspan="8"><strong>Diffusion models</strong></td></tr><tr><td>Pinaya, Graham, et al., 2022</td><td>WMH (WMH)</td><td>MRI (FLAIR)</td><td>2D</td><td>0.298</td><td>-</td><td>-</td><td>Transformer</td></tr><tr><td></td><td>MS (MSLUB)</td><td>MRI (FLAIR)</td><td>2D</td><td>0.247</td><td>-</td><td>-</td><td></td></tr><tr><td></td><td>Tumour(BraTS)</td><td>MRI (FLAIR)</td><td>2D</td><td>0.398</td><td>-</td><td>-</td><td></td></tr><tr><td>Wyatt et al., 2022 *</td><td>Tumour (Pernet et al.<fnref n="7" />)</td><td>MRI (T1)</td><td>2D</td><td>0.383</td><td>0.863</td><td>-</td><td>Simplex</td></tr><tr><td>Behrendt et al., 2023 *</td><td>Tumour (BraTS)</td><td>MRI (T2)</td><td>2D</td><td>0.490</td><td>-</td><td>0.541</td><td>Patch DDPM</td></tr><tr><td></td><td>MS (MSLUB)</td><td>MRI (T2)</td><td>2D</td><td>0.105</td><td>-</td><td>0.106</td><td></td></tr><tr><td>Bercea et al., 2023 *</td><td>Stroke (ATLAS)</td><td>MRI (T1)</td><td>2D</td><td>0.228</td><td>-</td><td>0.145</td><td>Conditioning</td></tr><tr><td>Iqbal et al., 2023 *</td><td>Tumour (BraTS)</td><td>MRI (T2)</td><td>2D</td><td>0.530</td><td>-</td><td><strong>0.590</strong></td><td>Masked DDPM</td></tr><tr><td></td><td>MS (MSLUB)</td><td>MRI (T2)</td><td>2D</td><td>0.107</td><td>-</td><td>0.106</td><td></td></tr><tr><td>Behrendt et al., 2024 *</td><td>Tumour (BraTS)</td><td>MRI (T2)</td><td>3D</td><td>0.574</td><td>-</td><td>-</td><td>SSIM</td></tr><tr><td></td><td>Stroke (ATLAS)</td><td>MRI (T1)</td><td>3D</td><td>0.148</td><td>-</td><td>-</td><td></td></tr><tr><td></td><td>MS (MSLUB)</td><td>MRI (T2)</td><td>3D</td><td>0.061</td><td>-</td><td>-</td><td></td></tr><tr><td></td><td>WMH (WMH)</td><td>MRI (T1)</td><td>3D</td><td>0.132</td><td>-</td><td>-</td><td></td></tr><tr><td>Bercea et al., 2024 *</td><td>Stroke (ATLAS)</td><td>MRI (T1)</td><td>2D</td><td>0.297</td><td>-</td><td>-</td><td>Conditioning</td></tr><tr><td>Fontanella et al., 2024</td><td>Tumour (BraTS)</td><td>MRI (T1, T1c, T2, FLAIR)</td><td>2D</td><td>0.699</td><td>-</td><td>-</td><td>DDIM</td></tr><tr><td></td><td>WMH (WMH)</td><td>MRI (FLAIR)</td><td>2D</td><td>0.569</td><td>-</td><td>-</td><td></td></tr></tbody></table>

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<footnote n="7">⁷Pernet et al., 2016</footnote>
<table><thead><tr><th>Reference</th><th>Pathology (Dataset)</th><th>Modality</th><th>Dim.</th><th>Dice</th><th>AUROC</th><th>AUPRC</th><th>Keyword</th></tr></thead><tbody><tr><td rowspan="2">Kumar Trivedi et al.,<br>2024 *</td><td>Tumour (BraTS)</td><td>MRI (T2)</td><td>2D</td><td>0.506</td><td>–</td><td>0.578</td><td>Patch DDPM</td></tr><tr><td>MS (MSLUB)</td><td>MRI (T2)</td><td>2D</td><td>0.055</td><td>–</td><td>0.067</td><td></td></tr><tr><td>Bi et al., 2025 *</td><td>Tumour (BraTS)</td><td>MRI (FLAIR)</td><td>2D</td><td><strong>0.738</strong></td><td><strong>0.922</strong></td><td>–</td><td>Multi-stage inference</td></tr><tr><td colspan="8"><strong>Self-supervised and others</strong></td></tr><tr><td>Kascenas et al., 2022</td><td>Tumour (BraTS)</td><td>MRI<br>(T1, T1c, T2, FLAIR)</td><td>2D</td><td>0.742</td><td>–</td><td>0.811</td><td>CNN extractor</td></tr><tr><td>Baugh et al., 2023 *</td><td>Tumour (BraTS)</td><td>MRI (T2)</td><td>2D</td><td>–</td><td>0.922</td><td>–</td><td>Self-supervised</td></tr><tr><td>Bercea, Wiestler, et al.,<br>2025 *</td><td>Stroke (ISLES)</td><td>MRI (FLAIR)</td><td>2D</td><td>–</td><td>0.846</td><td>–</td><td></td></tr><tr><td>Xiao et al., 2025</td><td>Tumour (Pernet et al. <sup>8</sup>)</td><td>MRI (T1)</td><td>N/A</td><td>–</td><td>–</td><td>–</td><td>Metrics</td></tr><tr><td>Ma et al., 2025</td><td>Tumour (BraTS)</td><td>MRI (FLAIR)</td><td>3D</td><td>0.856</td><td>–</td><td>–</td><td>Foundation models</td></tr><tr><td>X. Zhang et al., 2025 *</td><td>Tumour (BraTS)</td><td>MRI<br>(T1, T1c, T2, FLAIR)</td><td>3D</td><td>0.780</td><td>–</td><td>–</td><td>2-stage</td></tr><tr><td></td><td>Stroke (ISLES)</td><td>MRI (FLAIR)</td><td>3D</td><td>0.553</td><td>–</td><td>–</td><td></td></tr></tbody></table>

<footnote n="8">⁸Pernet et al., 2016</footnote>
**3.5 Synthesis by architecture**

We organised results by method family and pathology, reporting segmentation (Dice) when available and detection metrics (AUROC/AUPRC) otherwise. Per-study details (dataset, dimensionality, scores, code availability) are provided in Table 2, and grouped bar plots (mean ± standard deviation) of Dice scores across families and pathologies are shown in Fig. 3.

**3.5.1 Autoencoders**

While autoencoders are not strictly generative models, as they primarily learn to reconstruct inputs rather than model the underlying data distribution, we include them here because they laid the groundwork for subsequent generative approaches such as Variational Autoencoders, which have been widely used for unsupervised anomaly detection in medical imaging.

**Study characteristics.** We identified 11 studies employing autoencoder (AE) frameworks for unsupervised anomaly detection (UAD) in neuroimaging (Table 2). Most targeted brain tumours (Baur, Wiestler, et al., 2021; Behrendt et al., 2022; Ghorbel et al., 2023; Jiménez-García et al., 2024; Kascenas et al., 2023; Lu et al., 2024; Luo et al., 2023; Meissen et al., 2023), with further applications to multiple sclerosis (MS) (Baur, Wiestler, et al., 2021; Baur et al., 2018; Ghorbel et al., 2023; Luo et al., 2023). Single studies addressed stroke (Luo et al., 2023), Alzheimer's disease (Avci et al., 2024), white matter hyperintensities (WMH) (Baur, Wiestler, et al., 2021), Parkinson's disease (Muñoz-Ramírez et al., 2022), and healthy ageing (Meissen et al., 2023). All studies used MRI as the imaging modality (T1-w, T1c, T2-w, FLAIR), with one study employing diffusion tensor imaging (DTI) for Parkinson's disease. Six studies used 2D slice-wise inputs, while five adopted volumetric 3D AEs. The included AE studies were published between 2018 and 2024, spanning early dense-bottleneck models (Baur et al., 2018) to recent 3D convolutional and loss-tailored approaches (Avci et al., 2024; Jiménez-García et al., 2024; Lu et al., 2024).

**Architecture recap.** Autoencoders (AEs) were among the first deep learning architectures applied to UAD in neuroimaging. They reconstruct healthy anatomy from latent representations, with anomalies identified from residual differences between input and reconstruction. Architecturally, an AE consists of an encoder that compresses the input into a low-dimensional latent representation and a decoder that reconstructs the image back into the original space. Early implementations relied on fully connected (dense) bottlenecks, which limited spatial context and produced blurred reconstructions. Later studies
adopted convolutional layers, residual blocks to stabilise deeper networks (He et al., 2016), and attention
mechanisms inspired by vision transformers (Dosovitskiy et al., 2021; Ghorbel et al., 2023), improving
receptive fields and preservation of fine anatomical detail.

**Architectural trends and innovations.** One of the first AE UAD frameworks in neuroimaging was introduced by Baur et al. (2018) on MS and tumour MRI data, showing that dense AEs produced blurred reconstructions that limited localisation. Subsequent work proposed convolutional AEs with skip connections trained on whole-brain volumes rather than 2D patches, substantially improving detection of both large and small lesions (Baur, Denner, et al., 2021). At the interface between denoising and reconstruction, denoising autoencoders (DAEs) treat pathological regions as *noise* and replace them with healthy tissue patterns, revealing anomalies via input–output differences (Kascenas et al., 2023).

Beyond baseline reconstruction, several studies modified training inputs or losses. Jiménez-García et al. (2024) applied random 3D patch elastic deformations during reconstruction, while Avci et al. (2024) used deformation fields as direct inputs to the AE as proxy representations; both strategies improved Dice. Loss functions evolved as well: Lu et al. (2024) combined patch-wise contrastive and discriminative terms with mean squared error, yielding sharper localisation of subtle anomalies. Pretrained CNNs upstream of AEs have also been used to guide latent representations and increase sensitivity to textural abnormalities (Meissen et al., 2023).

Dataset purity emerged as a key factor: Behrendt et al. (2022) showed that introducing only 3% patho-
logical cases into the *healthy* training set reduced tumour-detection AUROC from 93.5 to 88.9. Di-
mensionality was likewise critical. While early works used 2D slice-wise processing, medical images are
inherently 3D; 3D AEs capture context more faithfully. However, Luo et al. (2023) highlighted that
latent dimensionality must be tuned carefully - too small yields oversmoothing, too large reintroduces
lesion features. A latent size of *z* = 512 provided the best balance across tumours, stroke, and MS.

**Quantitative synthesis.** Performance differed substantially across pathologies (Table 2). In brain
tumours, reported Dice ranged from 0.39 to 0.77, AUROC from 0.77 to 0.99, and the best AUPRC
reported is 0.83 (Baur, Wiestler, et al., 2021; Behrendt et al., 2022; Ghorbel et al., 2023; Jiménez-García
et al., 2024; Kascenas et al., 2023; Lu et al., 2024; Luo et al., 2023; Meissen et al., 2023). For MS, Dice
ranged from 0.17 to 0.65, AUROC up to 0.89, and AUPRC from 0.20 to 0.73 (Baur, Wiestler, et al.,
2021; Baur et al., 2018; Ghorbel et al., 2023; Luo et al., 2023). For stroke, a 3D AE achieved AUROC
0.81 and AUPRC 0.71 (Luo et al., 2023), while WMH segmentation reached Dice 0.45 and AUPRC 0.37
(Baur, Wiestler, et al., 2021). Other conditions included Alzheimer's disease (AUROC 0.80 (Avci et al.,
2024)), Parkinson's disease on DTI (AUROC 0.68 (Muñoz-Ramírez et al., 2022)), and healthy ageing (Dice 0.34, AUROC 0.78 (Meissen et al., 2023)).

**Closing.** Across the included studies, autoencoders showed their best performance for large and well-contrasted lesions such as tumours, with Dice values up to 0.77 and AUROC values approaching 0.99. For smaller or sparse abnormalities including MS, WMH, and stroke, Dice scores were substantially lower, often below 0.50. Reported outcomes depended strongly on training-set purity and dimensionality, with 3D models generally outperforming 2D implementations.

### 3.5.2 Variational autoencoders

**Study characteristics.** We included 13 records using variational autoencoders (VAEs) as the main UAD method (Table 2). Of these, nine focused on brain tumours (Bengs et al., 2021; Chatterjee et al., 2022; Lambert et al., 2021; Lüth et al., 2023; Pinaya et al., 2022; Sato et al., 2019; Uzunova et al., 2019; Wijanarko et al., 2024; Zimmerer et al., 2019), with three also addressing stroke (Bengs et al., 2021; Lüth et al., 2023; Sato et al., 2019). WMH were studied by Pinaya et al. (2022) and Lambert et al. (2021) (the latter combining WMH with MS due to visual similarities). Two studies investigated Alzheimer's disease (Hassanaly et al., 2024; Solal et al., 2023). Single studies examined neonatal anomalies (Neonatal encephalopathy) (Raad et al., 2023) and MS exclusively (Pinaya et al., 2022). Two studies employed healthy datasets alongside synthetic lesion generators (Chatterjee et al., 2022; Huijben et al., 2024). With the exceptions of (Hassanaly et al., 2024; Solal et al., 2023) (PET), all others employed MRI (T1-w, T1c, T2-w, FLAIR). There was a 6:7 split between 2D and 3D VAEs.

**Architecture recap.** VAEs extend the autoencoder framework by introducing a probabilistic latent space. Instead of mapping each input deterministically to a single code, the encoder outputs the parameters of a probability distribution - typically a Gaussian defined by mean and variance. This latent distribution is regularised to match a simple prior, most often a standard multivariate normal $N(0, I)$. During training, latent samples are drawn from this distribution and passed through the decoder to reconstruct the input. The learning objective therefore combines a reconstruction error with a Kullback-Leibler (KL) divergence term that enforces similarity between the learned latent distribution and the prior (Kingma & Welling, 2013). This probabilistic formulation encourages smooth latent representations, improves generalisation, and allows new samples to be generated directly from the prior.
**Architectural trends and innovations.** Beyond the canonical VAE, several major extensions have been proposed to improve reconstruction fidelity and anomaly localisation. One such refinement is the *spatial VAE*, where the latent representation is preserved as a low-resolution feature map rather than collapsed into a single dense vector. This spatial structure maintains correspondence between latent units and image regions, enabling anatomically more faithful reconstructions and improved segmentation performance, particularly in 3D neuroimaging (Bengs et al., 2021; Lambert et al., 2021). Another important extension is the Vector Quantised VAE (VQ-VAE) (Oord et al., 2017), which replaces the continuous latent space with a discrete codebook of embeddings. By enforcing quantisation, VQ-VAEs capture more global and semantically meaningful features, expanding the receptive field and improving the modelling of long-range dependencies. Building on this design, Pinaya et al. (2022) introduced an autoregressive transformer trained on healthy data to *heal* pathological latent codes in brains affected by tumours or MS, mapping unhealthy codes to their closest healthy equivalents.

Loss design has been another major direction. Sato et al. (2019) removed the log-variance term from the reconstruction loss, arguing that it primarily captures normal anatomical variation rather than pathology. Retaining only the squared error term improved robustness to fine anatomical details and increased stroke-detection AUROC on the ATLAS dataset by 6.1 points. Similarly, Wijanarko et al. (2024) proposed a triplet-VAE with three parallel branches (anchor, positive, negative), all sharing weights. Their multi-component loss combined L1 terms, KL divergence between anchor and positive, a weighted sum of L2 reconstruction errors, and the Structural Similarity Index Measure (SSIM) applied to the negative input, achieving Dice 0.61 and AUROC 0.98 for tumour segmentation and detection.

Auxiliary and hybrid extensions have also been proposed. Context-encoding VAEs (ceVAEs) augment a standard VAE with a deterministic masked-image reconstruction branch that shares the same encoder-decoder weights. While the VAE optimises the usual reconstruction and KL divergence losses, the additional branch is trained to inpaint missing regions of the input, encouraging context-aware features. The combined losses are backpropagated jointly through the shared network. Chatterjee et al. (2022) demonstrated this approach with task-specific pre- and post-processing tailored for anomaly detection. Lüth et al. (2023) further introduced a contrastive pretraining stage, mapping semantically similar subjects closer in latent space before training the ceVAE, and also investigated alternative decoders such as Gaussian mixture models (Koller & Friedman, 2009) and normalising flows (Rezende & Mohamed, 2015), showing that decoder choice influences anomaly localisation.

Conditioning mechanisms have also been explored. Uzunova et al. (2019) applied positional encodings to 2D and 3D patches, finding improvements over AnoGAN (Schlegl et al., 2017) in 2D but reduced performance in 3D due to limited model capacity. Beyond architecture, training behaviour has been
scrutinised. Huijben et al. (2024) showed that the epoch with the lowest reconstruction loss often does not yield the best anomaly detection, and highlighted sensitivity to hyperparameters such as convolutional filter and bottleneck sizes.

Finally, anomaly scoring strategies have evolved. Zimmerer et al. (2019) proposed ELBO-informed scores, where the anomaly map is based not only on reconstruction error but also on the KL term of the evidence lower bound (ELBO), reflecting how well the latent distribution matches the prior. Similarly, Huijben et al. (2024) showed that perceptual metrics such as the Learned Perceptual Image Patch Similarity (LPIPS (R. Zhang et al., 2018)), which compares feature representations from pretrained networks rather than raw pixels, outperform simple reconstruction error in detecting subtle anomalies.

**Quantitative synthesis.** In brain MRI, 3D VAEs generally achieved higher detection and segmentation scores than their 2D counterparts (Bengs et al., 2021; Lambert et al., 2021). Within the 3D category, spatial VAEs provided a further advantage by preserving latent maps instead of collapsing them into dense vectors. This spatial structure allowed more faithful reconstructions of anatomy and translated into a larger improvement in tumour segmentation accuracy, with gains of +12.6 Dice points compared to the modest +3.2 points observed for dense bottleneck VAEs. The best-performing configuration was a 3D spatial VAE, which achieved a Dice score of 0.65 (excluding synthetic lesions) (Bengs et al., 2021). While this still fell short of a fully supervised 3D baseline (approximately 0.74), the gap was substantially smaller than for earlier unsupervised approaches.

Beyond MRI applications, 3D VAEs have been successfully applied to FDG-PET for Alzheimer's disease detection (Hassanaly et al., 2024; Solal et al., 2023), and also showed promise in neonatal brain anomaly detection, a task ill-suited to supervised methods due to the scarcity of annotated data, diversity of lesions and the inherently low MRI contrast in newborns (Johnson et al., 1983), achieving an AUROC of 0.83 (Raad et al., 2023).

Segmentation performance varied across lesion types. Tumour Dice scores ranged from 0.30 to 0.65, with detection metrics spanning 0.58–0.96 (AUROC) and 0.28–0.82 (AUPRC) (Bengs et al., 2021; Lambert et al., 2021; Lüth et al., 2023; Pinaya et al., 2022; Sato et al., 2019). For stroke, reported Dice scores reached 0.33, with AUROC between 0.67 and 0.69 and AUPRC around 0.26 (Bengs et al., 2021; Lüth et al., 2023; Sato et al., 2019). MS showed comparable results, with a Dice of 0.38 and AUPRC of 0.27 (Pinaya et al., 2022). The highest Dice was reported on synthetic lesions, reaching 0.72 (Chatterjee et al., 2022), though the limited lesion variability (20 synthetic cases) likely led to optimistic performance estimates.
**Closing.** Compared with standard autoencoders, VAEs introduced a probabilistic latent space and, in some cases, improved anatomical fidelity and segmentation accuracy, particularly for brain tumours when using spatial or VQ variants. However, across pathologies such as MS, WMH, and stroke, performance gains over AEs were limited or inconsistent, with several studies reporting comparable or lower Dice and detection values. Overall, VAEs offered architectural flexibility but did not consistently outperform AE baselines across lesion types.

### 3.5.3 Generative adversarial networks

**Study characteristics.** We identified eight studies employing generative adversarial networks (GANs) for unsupervised anomaly detection in neuroimaging (Table 2). Most focused on brain tumours using BraTS MRI data (Bougaham et al., 2025; Dey et al., 2021; Nguyen et al., 2021; Wu et al., 2021), with others addressing multiple sclerosis lesions on MSSEG (Dey et al., 2021), traumatic brain injury with CT from the Center-TBI cohort (Simarro et al., 2020), and Alzheimer's disease with ADNI and OASIS-3 (Cabreza et al., 2022; Rahman Siddiquee et al., 2024). A further study extended f-AnoGAN to retinal OCT for macular oedema detection (Schlegl et al., 2019). Most approaches used 2D slice-wise GANs, though volumetric 3D architectures were applied to TBI and tumours (Simarro et al., 2020; Wu et al., 2021).

**Architecture recap.** GANs, introduced by Goodfellow et al. (2014), consist of two networks trained in opposition: a generator that produces synthetic samples and a discriminator that distinguishes real from generated data. Through this adversarial process, the generator learns to approximate the training distribution. In neuroimaging UAD, the generator is trained on healthy brain images to reconstruct pseudo-healthy counterparts of pathological inputs, with anomalies identified from residual differences. Compared with VAEs, GANs can produce sharper and more realistic reconstructions (Bond-Taylor et al., 2022), but they remain prone to instability and mode collapse, where only a limited set of patterns are generated reliably, limiting domain shift and applicability to clinical data (Ackaouy et al., 2020; Ghafoorian et al., 2017). The most widely recognised GAN architecture for medical UAD is f-AnoGAN (Schlegl et al., 2019), an improved version of AnoGAN (Schlegl et al., 2017) that introduced an encoder for faster inference and a Wasserstein loss for greater training stability.

**Architectural trends and innovations.** Several adaptations have been developed to address the limitations of GANs in medical UAD, particularly unstable training, coarse anomaly maps, and low sensitivity to subtle lesions. First, methods aimed to improve the anatomical plausibility of reconstructions. Cycle-
consistent models enforced bidirectional mappings between pathological and healthy domains, ensuring that round-trip translations preserved structural fidelity (Bougaham et al., 2025), while symmetry-driven GANs exploited contralateral hemispheres as pseudo-healthy priors to improve localisation of unilateral tumours (Wu et al., 2021).

Second, sensitivity to subtle disease signatures was increased through attention mechanisms, which highlighted cortical and subcortical changes characteristic of Alzheimer's disease (Cabreza et al., 2022), and through partition-based approaches that explicitly separated normal from anomalous regions before adversarial evaluation (Dey et al., 2021).

Third, output quality was improved by refining anomaly maps. Two-stage pipelines added a super-resolution module to sharpen otherwise coarse reconstructions and improve tumour segmentation Dice scores (Nguyen et al., 2021). And patch-level discriminators reduced memory demands and increased sensitivity to localised abnormalities by restricting adversarial training to image subregions rather than entire volumes (Rahman Siddiquee et al., 2024).

Taken together, these innovations reflect a shift from generic adversarial frameworks toward task-specific adaptations that enhance stability, localisation precision, and clinical interpretability in neuroimaging UAD.

**Quantitative synthesis.** Performance of GAN-based methods varied substantially across pathologies and datasets (Table 2). In brain tumours, Dice scores ranged from 0.62 to 0.77 across different architectures, with the lowest value reported for a 3D symmetry-driven GAN (Wu et al., 2021) and the highest for a two-stage refinement GAN (Nguyen et al., 2021). Detection performance was similarly strong, with AUROC values reaching 0.93 using a cycle-consistent GAN on BraTS (Bougaham et al., 2025). Multiple sclerosis lesions proved more challenging to segment, with a maximum Dice score of only 0.48 on MSSEG (Dey et al., 2021). Traumatic brain injury was evaluated on CT from the Center-TBI cohort, where a 3D f-AnoGAN achieved AUROC 0.75 (Simarro et al., 2020). Alzheimer's disease detection yielded more variable outcomes, with AUROC 0.66 on ADNI (Rahman Siddiquee et al., 2024) and 0.80 on OASIS-3 (Cabreza et al., 2022). Across studies, AUPRC values were rarely reported, and when segmentation was attempted, lesion maps were generally coarse and required additional refinement to approach the accuracy of supervised baselines.

**Closing.** GAN-based approaches generated visually sharp pseudo-healthy reconstructions and reported high AUROC values for tumour detection. Tumour segmentation reached moderate Dice values, which improved when incorporating cycle-consistency, symmetry priors, or refinement modules. For MS and
neurodegenerative disorders, performance was lower, and outputs were frequently coarse unless supplemented by post-processing.

### 3.5.4 Denoising diffusion probabilistic models

**Study characteristics.** We identified nine studies applying diffusion models to UAD in neuroimaging (Table 2). Most addressed brain tumour detection and segmentation (Behrendt et al., 2023, 2024; Fontanella et al., 2024; Iqbal et al., 2023; Kumar Trivedi et al., 2024; Pinaya, Graham, et al., 2022; Wyatt et al., 2022), with additional work on stroke (Behrendt et al., 2024; Bercea et al., 2023, 2024), MS (Behrendt et al., 2023, 2024; Iqbal et al., 2023; Kumar Trivedi et al., 2024; Pinaya, Graham, et al., 2022), and WMH (Behrendt et al., 2024; Fontanella et al., 2024; Pinaya, Graham, et al., 2022). All but one study (Behrendt et al., 2024) processed data in a 2D slice-wise manner.

**Architecture recap.** Denoising diffusion probabilistic models (DDPMs), or diffusion models, learn an iterative denoising process that transforms Gaussian noise into images drawn from a target distribution. Training involves two complementary steps. In the forward process, Gaussian noise is gradually added to a dataset sample through a Markov chain of typically 1,000 steps, until the signal is almost completely destroyed. In the reverse process, a neural network (commonly a U-Net (Ronneberger et al., 2015)) learns to invert this corruption step by step, reconstructing the image from pure noise.

Once trained, a diffusion model can generate new samples by drawing from a Gaussian distribution and applying the learned reverse process. Generation can also be conditioned on additional information, such as text descriptions (Nichol et al., 2022) or segmentation masks (Dorjsembe et al., 2024). A key limitation is that classical DDPMs operate directly in pixel or voxel space, which becomes computationally prohibitive for high-dimensional 3D images. To address this, latent diffusion models were introduced in Stable Diffusion (Rombach et al., 2022), where a VAE first compresses images into a lower-dimensional latent space. Diffusion is then trained in this space, with denoised latents decoded back into image space, reducing memory and compute requirements. Sampling efficiency has also been improved by non-Markovian variants such as denoising diffusion implicit models (DDIMs) (J. Song et al., 2021), which require fewer denoising steps while preserving image quality.

**Architectural trends and innovations.** In medical UAD, diffusion models are trained exclusively on healthy data, with anomalies revealed when pathological inputs are partially corrupted and regenerated. Anomaly maps are then derived from residual differences between input and reconstruction. Several
innovations have improved this framework.

*Noise design.* Early work used Gaussian noise, but structured noise patterns such as Perlin or Simplex yielded more accurate segmentations (Bercea et al., 2024; Wyatt et al., 2022).

*Similarity metrics.* Instead of raw intensity residuals, structural similarity index (SSIM) (Behrendt et al., 2024) and perceptual metrics such as LPIPS, which compare deep feature embeddings rather than pixels (Chen et al., 2019; R. Zhang et al., 2018), improved anomaly localisation by better capturing structural differences.

*Refinement strategies.* Strong noise injection helps expose anomalies but also corrupts normal anatomy. To mitigate this, some methods re-injected masked images after an initial denoising pass (Bercea et al., 2023), while others selectively reintroduced healthy regions during denoising, yielding more plausible reconstructions and improved lesion segmentation (Bercea et al., 2024).

*Latent-space hybrids.* Inspired by Stable Diffusion, Pinaya, Graham, et al. (2022) extended their earlier VQ-VAE approach by embedding it within a latent diffusion framework. Kumar Trivedi et al. (2024) introduced a bridge network to combine latent representations from partially and fully noised images, which were then passed to the diffusion U-Net.

*Patch- and mask-based designs.* Patch-based diffusion models (pDDPM) applied noise only to selected regions, reducing computational cost while focusing reconstructions on potentially anomalous areas (Behrendt et al., 2023). Masked DDPMs (mDDPM) extended this by cutting out patches or masking Fourier components, further improving anomaly localisation (Iqbal et al., 2023).

*Hybrid frameworks.* More recent work combines diffusion with saliency-driven or counterfactual strategies. For example, Fontanella et al. (2024) proposed a two-stage scheme where saliency maps generated by ATAC (Anatomical Taboo Augmented Contrastive learning (Fontanella et al., 2023)) guide a DDIM to regenerate healthy regions while replacing anomalous parts via DDPM. While promising in segmentation accuracy, such pipelines rely on supervised components and thus fall outside strict unsupervised paradigms.

*Multi-stage inference.* To generate more realistic counterfactuals, Bi et al. (2025) introduced a multi-stage inference strategy. In this approach, the image is iteratively processed through a cascade of diffusion passes, with each stage progressively attenuating the pathological features. 5-stage inference cascade reportedly improved the dice score by 12 points.
**Quantitative synthesis.** Diffusion-based UAD showed heterogeneous performance across pathologies and dimensionalities.

For brain tumours, 2D Dice scores ranged from 0.30 to 0.74, while 3D models showed a performance of 0.57 in the largest volumetric evaluation (Behrendt et al., 2024). Stroke remained challenging, with Dice scores between 0.15 and 0.30 (Behrendt et al., 2024; Bercea et al., 2024). MS yielded the lowest values, from 0.06 to 0.25 (Kumar Trivedi et al., 2024; Pinaya, Graham, et al., 2022). WMH performed somewhat better, with Dice scores between 0.13 and 0.57 (Behrendt et al., 2024; Fontanella et al., 2024).

A notable outlier was a hybrid, partially supervised pipeline reporting Dice of 0.57 for WMH segmentation (Fontanella et al., 2024), but its reliance on supervised saliency maps makes it not directly comparable to strictly unsupervised methods.

In detection metrics, tumours were generally easiest to identify, with AUPRC values of 0.54-0.59 (Behrendt et al., 2023; Iqbal et al., 2023), whereas MS and stroke were considerably more difficult, with AUPRC values around 0.11 and 0.15, respectively (Behrendt et al., 2023; Bercea et al., 2023; Iqbal et al., 2023).

**Closing.** Across the included studies, diffusion models produced anatomically realistic reconstructions and achieved their highest accuracy for tumours, with Dice scores up to 0.74 in 2D and 0.57 in 3D. Stroke performance was lower, with Dice values between 0.15 and 0.30, and MS was the most challenging, typically below 0.25; WMH showed intermediate values, with Dice up to 0.57. Detection metrics reflected the same pattern, with AUPRC above 0.50 for tumours but markedly lower for smaller or diffuse lesions. Reported architectural adaptations - including structured noise, perceptual similarity measures, selective masking, cascaded inference, and latent-space hybrids - improved reconstruction fidelity and anomaly localisation, yet overall accuracy remained strongly dependent on lesion size and pathology type.

### 3.5.5 Related non-generative approaches and evaluation frameworks

Although our eligibility criteria excluded supervised or semi-supervised methods, we summarise closely related alternatives reported alongside generative UAD to contextualise results; these are not included in quantitative comparisons.

In addition to the four method families (AEs, VAEs, GANs, and diffusion), we identified a smaller group of non-generative approaches. These methods do not reconstruct pseudo-healthy images; instead, they
detect anomalies through synthetic self-supervision, discriminative learning, or likelihood-based scoring. We also include one study that did not propose a new detection method but instead focused on evaluation metrics across UAD architectures.

**Study characteristics.** We identified six studies in this category. Four introduced alternative anomaly detection approaches: three focused on brain tumours (Kascenas et al., 2022; Ma et al., 2025; Xiao et al., 2025), and two combined tumours and ischaemic stroke data (Baugh et al., 2023; X. Zhang et al., 2025). The sixth study addressed evaluation by proposing novel metrics for comparing UAD methods across architectures (Bercea, Wiestler, et al., 2025).

**Architectural trends and innovations.** Unlike generative models, these approaches do not produce pseudo-healthy reconstructions. Instead, they rely on four distinct strategies:

*Synthetic self-supervision.* X. Zhang et al. (2025) proposed a two-stage framework in which artificial tumours and masks were synthesised using shape and intensity models, enabling supervised training of a U-Net. Baugh et al. (2023) introduced a pathology-agnostic approach that trained models on diverse auxiliary tasks such as patch blending, geometric deformations, and intensity variations. Exposure to this diversity of synthetic anomalies improved tumour and stroke detection compared with classical and context-encoding VAEs.

*Foundation models.* Alternatively, Ma et al. (2025) employed a self-supervised approach that leverages two foundation models. First, a self-supervised classifier was trained on pseudo-labels generated by a CLIP encoder (Radford et al., 2021). The resulting saliency maps were then fed to a foundational segmentation model (Kirillov et al., 2023), which synthesised segmentation masks. These masks were ultimately used to train a 3D U-Net in a self-supervised fashion.

*Discriminative anomaly detection.* Kascenas et al. (2022) trained a fully convolutional classifier on pairs of masked images and candidate patches. By deliberately generating mismatched pairs during training, the model learned to distinguish normal from anomalous content.

*Likelihood-based scoring.* Xiao et al. (2025), inspired by industrial anomaly detection (Rudolph et al., 2021), combined CNN features with a normalising flow model. Likelihood estimates of feature vectors were then thresholded to produce anomaly scores without requiring reconstruction.

**Metrics.** In contrast to the above four studies, which introduced detection methods, Bercea, Wiestler, et al. (2025) focused on evaluation, reframing UAD as a problem of *normative representation learning*
- the ability of models to capture and reproduce typical healthy anatomy. They argued that most anomaly detection studies assess only detection accuracy, often on obvious lesions, while overlooking this fundamental capability. To address this gap, they proposed three indices tailored to visual counterfactual explanations:

- *Restoration Quality Index (RQI)* - quantifies the fidelity of reconstructions using the perceptual similarity metric LPIPS (R. Zhang et al., 2018). - *Anomaly-to-Healthy Index (AHI)* - measures how plausibly a pathological image is transformed into a healthy counterpart, based on the Fréchet Inception Distance (FID). - *Conservation and Correction Index (CACI)* - evaluates whether reconstructions preserve healthy regions while selectively correcting anomalies, combining SSIM and related structural measures. These metrics were applied across AEs, VAEs, GANs, and diffusion models, and validated through a multi-reader study with 16 radiologists. The study showed that the proposed metrics aligned with radiologists' judgements and that models with stronger normative representations also tended to generalise better across unseen pathologies, highlighting the need to evaluate not only anomaly detection accuracy but also underlying normative modelling.

**Quantitative synthesis.** As Bercea, Wiestler, et al. (2025) did not propose a detection model, quantitative results are reported only for the five methodological studies (Table 2). For brain tumours, Dice scores reached 0.74, 0.78 and 0.86 (Kascenas et al., 2022; Ma et al., 2025; X. Zhang et al., 2025). For ischaemic stroke, X. Zhang et al. (2025) reported a Dice of 0.53. From a detection perspective, tumour AUROC values ranged from 0.91–0.92 (Baugh et al., 2023; Xiao et al., 2025), while ischaemic stroke detection achieved AUROC 0.85 (Baugh et al., 2023).

**Closing.** Non-generative approaches achieved strong performance for tumours, with Dice scores up to 0.86 and AUROC values above 0.90, while stroke results were more modest, with Dice around 0.53 and AUROC of 0.85. These methods bypassed reconstruction and instead relied on synthetic self-supervision, foundation models, discriminative learning, or likelihood-based scoring. In addition, dedicated evaluation metrics were introduced to quantify normative representation quality, providing complementary measures beyond conventional Dice and AUROC.

**3.6 Comparison across studies**

Unsupervised brain tumour segmentation has been investigated more extensively than other pathologies and generally achieves the highest Dice scores (Figure 3a). Among the four model families, GAN-based
methods reported the highest mean Dice of 0.69 ± 0.08, while VAEs, AEs, and diffusion models achieved broadly comparable results of 0.51 ± 0.11, 0.50 ± 0.14, and 0.52 ± 0.12, respectively.

For smaller or sparser lesions such as MS, performance dropped across all families (Figure 3c). Diffusion models underperformed (0.11 ± 0.08), while AEs (0.48 ± 0.26), VAEs (0.38), and GANs (0.48) achieved only moderate segmentation quality. WMH followed the same trend (Figure 3d), consistent with their radiological resemblance to MS lesions.

Sub-acute and chronic stroke segmentation has been less frequently studied, with only five studies available (Figure 3b). Across these, average Dice scores remained lower than for tumours, reflecting the less sharply defined boundaries and heterogeneous appearance of ischaemic lesions (Hernandez Petzsche et al., 2022). No consistent differences between model families can be inferred from this limited evidence.

Taken together, these comparisons indicate that tumours represent the most tractable application for unsupervised anomaly detection, whereas MS, WMH, and stroke remain substantially more challenging. Mean Dice values for these latter conditions consistently fell below 0.50, regardless of architecture. Differences between model families were smaller than differences between pathologies, though GANs achieved slightly higher mean Dice for tumours and diffusion models produced the most anatomically realistic reconstructions. Importantly, results must be interpreted with caution, as the underlying studies often used different datasets, evaluation protocols, and preprocessing pipelines, limiting the comparability of absolute values across methods.

# 4 Discussion

**Principal findings.** This scoping review synthesised seven years of research (2018-2025) on unsupervised deep generative models for neuroimaging anomaly detection and segmentation. We identified four main families - autoencoders (AEs), variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models - together with related non-generative approaches. Across methods, tumours (typically large and hyperintense) were the most tractable anomalies: mean Dice scores were modest to moderate and highest for GAN-based approaches, while AEs and VAEs yielded broadly comparable segmentation and detection performance (Fig. 3). By contrast, small or sparse abnormalities such as multiple sclerosis (MS), white matter hyperintensities (WMH), and stroke remained far more challenging, with mean Dice scores typically below 0.50. Diffusion models produced anatomically realistic reconstructions but did not consistently outperform AE/VAE approaches in terms of lesion segmentation. Stroke remained challenging despite lesion sizes comparable to tumours, likely due to heterogeneous
<figure><img src="image_4.png" /><figcaption>(a) Tumour</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(b) Stroke</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(c) Multiple Sclerosis (MS)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(d) White Matter Hyperintensities (WMH)</figcaption></figure>

Figure 3: Mean Dice scores (± SD) of unsupervised anomaly detection methods across pathologies. Reported values are derived from different datasets, preprocessing pipelines, and evaluation protocols; therefore, absolute values are not directly comparable between families and should be interpreted as indicative trends rather than head-to-head benchmarks.
appearance and ill-defined boundaries. None of the unsupervised families matched state-of-the-art su-
pervised baselines on BraTS (Dice > 0.9 is now routine <sup>9</sup>). Nonetheless, unsupervised approaches retain
clear advantages where voxel-level annotations are scarce or unobtainable, and for producing visual counter-
factual explanations that may aid clinical interpretability.

**Results interpretation.** Two main themes emerged from our synthesis: the influence of pathology characteristics and the influence of architectural design. First, pathology type had a stronger effect on performance than architectural family. Tumours were generally the most tractable, with moderate Dice scores across all models, while stroke, MS, and WMH proved far more challenging. The relative weakness of diffusion approaches on MS and WMH reflects their sensitivity to lesion size and contrast. Stroke, despite lesion volumes comparable to tumours, consistently showed lower scores due to heterogeneous appearance and ill-defined boundaries. Second, architectural choices and training strategies shaped, but did not overturn, these pathology-driven patterns. Diffusion models produced the most anatomically realistic reconstructions, but their segmentation accuracy lagged on small or sparse lesions. GANs achieved slightly higher mean Dice for tumours, while AEs and VAEs yielded broadly comparable results. Across families, 3D models tended to outperform 2D variants by capturing richer anatomical context, although gains were often modest and sometimes reversed for small lesions due to volumetric class imbalance. Design refinements such as patching, masking, loss tailoring, and pretraining consistently boosted performance and often reduced computational cost. Compared with fully supervised methods, all unsupervised families remain well below benchmark tumour segmentation accuracy. Yet, they remain valuable where annotations are limited, such as rare diseases, neonatal imaging, or multi-centre studies with heterogeneous protocols. A unique strength is their ability to generate *pseudo-healthy reconstructions*, providing visual counterfactuals that parallel the radiologist's mental comparison of observed vs. expected anatomy (Waite et al., 2019). This interpretability advantage complements, rather than replaces, supervised "black-box" segmenters.

Thus, while supervised segmentation remains state of the art, its dependence on large voxel-level datasets
constrains generalisation. Unsupervised generative models, though less accurate, offer pathology-agnostic
detection, interpretable reconstructions, and potential roles as anomaly detectors, triage tools, and
hypothesis-generating frameworks for novel imaging biomarkers.

**Future directions.** Our synthesis highlights several priorities for advancing unsupervised anomaly detection in neuroimaging. The most persistent challenge is performance on small, sparse or fuzzy lesions

<footnote n="9">⁹See https://www.synapse.org/Synapse:syn53708249</footnote>
(e.g., MS, WMH, chronic stroke), where Dice scores consistently lag far behind those for large, hyperintense tumours. Overcoming this gap will require progress on both architectural innovation and evaluation practices.

On the architectural side, more efficient continuous-time generative models - such as flow matching (Lipman et al., 2023) or score-based diffusion variants (Y. Song et al., 2021) - offer promising alternatives to classical DDPMs, with faster sampling and potentially sharper reconstructions. Latent-space hybrids that pair strong encoders (e.g., VQ- or spatial VAEs) with diffusion or flow decoders could enable scalable 3D counterfactuals (Bengs et al., 2021; Lambert et al., 2021; Pinaya et al., 2022). Incorporating anatomy-aware priors (symmetry constraints, atlas guidance) and perceptually informed residuals (e.g., SSIM, LPIPS) may further improve localisation and reduce false positives (Behrendt et al., 2024; Chen et al., 2019; R. Zhang et al., 2018). Hybrid strategies that balance generative reconstruction with discriminative cues also merit exploration.

Equally important is rethinking evaluation. Current benchmarks rely heavily on Dice or AUROC computed on well-defined lesions, but these metrics do not capture the central goal of unsupervised methods: learning robust normative representations of healthy anatomy. As Bercea, Li, et al. (2025) emphasise, a model may score well on Dice yet still fail clinically if it misses subtle or sparse lesions. Task-specific indices that quantify the fidelity of pseudo-healthy reconstructions - such as those assessing how well healthy regions are preserved and how plausibly anomalies are corrected - provide a more meaningful measure of normative modelling. Likewise, benchmarks such as NOVA illustrate the importance of testing models on rare and heterogeneous pathologies, rather than only on widely used datasets like BraTS or MSSEG.

Finally, translation into clinical practice will depend on prospective validation. Counterfactual reconstructions could improve radiologists' confidence in subtle findings, prioritise abnormal cases in triage, and reduce time-to-decision in workflows. However, these potential benefits remain untested. Controlled reader studies are essential to determine whether generative reconstructions truly improve diagnostic performance - by improving accuracy, efficiency, or consistency across readers - rather than simply automating existing tasks. Demonstrating such added value is a critical step toward clinical adoption.

**Clinical implications.** Unsupervised generative models are best positioned as broad anomaly detectors and triage tools, especially where annotations are unavailable. Their pseudo-healthy reconstructions offer interpretable counterfactuals that complement supervised *black box* segmenters. However, for routine clinical segmentation, accuracy remains insufficient, particularly for small or sparse pathologies. Taken together, these comparisons highlight that lesion size and contrast strongly influence unsupervised anomaly detection performance: large, hyperintense tumours are segmented with moderate success,
whereas smaller lesions such as MS, WMH, and stroke remain challenging across all method families.

**Limitations.** This review has several limitations. First, although we searched five major databases (PubMed, Web of Science, ScienceDirect, Springer Nature Link, and ArXiv) and performed reference crawling, some relevant studies may have been missed, especially those not indexed in these sources. On ArXiv, restricting to the *Computer Science (cs)* filter may also have excluded relevant biomedical preprints. Second, because the field is rapidly evolving, our cut-off date of 8 September 2025 introduces temporal bias, with very recent work possibly underrepresented. Third, our synthesis relied on commonly reported metrics: Dice for segmentation, AUROC and AUPRC for detection. Each carries limitations. Dice is biased toward large lesions, as small errors disproportionately penalise small lesions such as MS or WMH. AUROC, while standard, can obscure class imbalance; AUPRC is often presented as an alternative but introduces its own biases and is not inherently superior (McDermott et al., 2024). Other measures (e.g., sensitivity, specificity, F1-score) were reported inconsistently and could not be systematically compared. Fourth, evaluation strategies varied across studies - for example, thresholds for binarising residual maps were sometimes optimised and sometimes fixed - directly influencing reported scores. Finally, as emphasised by Bercea, Wiestler, et al. (2025), conventional metrics such as Dice, AUROC, and AUPRC do not capture the core goal of unsupervised methods: learning robust normative representations. This highlights the need for task-specific metrics that better reflect clinical validity.

# 5 Conclusion

In this systematic scoping review, we compared generative AI-based methods for anomaly detection and segmentation in brain MRI, focusing on their ability to model healthy anatomy and detect deviations. None of the included studies solved the challenge across all pathologies. In detection, some methods achieved high AUROC values (> 0.9), but performance was typically pathology- or dataset-specific. No generalisable detection framework has yet emerged. Segmentation remains particularly challenging: Dice scores generally remained below 0.6 for large lesions and below 0.1 for small ones. We categorised studies by architecture (AE, VAE, GAN, diffusion) and summarised their main contributions (Table 2). Following PRISMA guidelines, we provided a transparent and reproducible synthesis, identifying consistent performance patterns across pathologies and highlighting emerging innovation. In summary, unsupervised generative models provide a valuable, annotation-free strategy for detecting and visualising neuroimaging anomalies. However, performance remains limited for small or sparse lesions, and these methods do not yet match supervised baselines. Future work should prioritise anatomy-aware architectures, stan-
dardised multi-pathology benchmarks, and prospective reader studies to establish whether counterfactual reconstructions can translate into clinically meaningful diagnostic support.

## Data and materials availability

All extracted data, screening records, and analysis code (including scripts for figure generation) are openly available in the supporting github repository<fnref n="10" />. The full PRISMA-ScR checklist is provided in the Supplementary Materials.

## Competing interests

The authors declare no conflicts of interest. No review protocol was prepared, and this review was not preregistered in PROSPERO or any other registry. Large language models (LLMs) were used exclusively for writing assistance, editing, and formatting. They did not contribute to study design, methodology, data analysis, or interpretation, and therefore did not affect the originality or scientific rigour of this work.

**CRediT authorship contribution statement** YM: Conceptualisation, Methodology, Investigation, Data curation, Writing – original draft. EB: Methodology, Supervision, Writing – review & editing. SL: Methodology, Supervision, Writing – review & editing. EF: Supervision, Writing – review & editing. FG: Methodology, Validation, Supervision, Writing – review & editing. All authors read and approved the final manuscript.

<footnote n="10">¹⁰https://github.com/youwanM/Unsupervised-Deep-Generative-Models-for-Anomaly-Detection-in-Neuroimaging</footnote>
Supplementary Material

Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for
Scoping Reviews (PRISMA-ScR) Checklist

<table><thead><tr><th>SECTION</th><th>ITEM</th><th>PRISMA-ScR CHECKLIST ITEM</th><th>REPORTED ON PAGE #</th></tr></thead><tbody><tr><td>TITLE</td><td></td><td></td><td></td></tr><tr><td>Title</td><td>1</td><td>Identify the report as a scoping review.</td><td>1</td></tr><tr><td>ABSTRACT</td><td></td><td></td><td></td></tr><tr><td>Structured summary</td><td>2</td><td>Provide a structured summary that includes (as applicable): background, objectives, eligibility criteria, sources of evidence, charting methods, results, and conclusions that relate to the review questions and objectives.</td><td>1</td></tr><tr><td>INTRODUCTION</td><td></td><td></td><td></td></tr><tr><td>Rationale</td><td>3</td><td>Describe the rationale for the review in the context of what is already known. Explain why the review questions/objectives lend themselves to a scoping review approach.</td><td>1-4</td></tr><tr><td>Objectives</td><td>4</td><td>Provide an explicit statement of the questions and objectives being addressed with reference to their key elements (e.g., population or participants, concepts, and context) or other relevant key elements used to conceptualize the review questions and/or objectives.</td><td>4-5</td></tr><tr><td>METHODS</td><td></td><td></td><td></td></tr><tr><td>Protocol and registration</td><td>5</td><td>Indicate whether a review protocol exists; state if and where it can be accessed (e.g., a Web address); and if available, provide registration information, including the registration number.</td><td>34</td></tr><tr><td>Eligibility criteria</td><td>6</td><td>Specify characteristics of the sources of evidence used as eligibility criteria (e.g., years considered, language, and publication status), and provide a rationale.</td><td>8</td></tr><tr><td>Information sources*</td><td>7</td><td>Describe all information sources in the search (e.g., databases with dates of coverage and contact with authors to identify additional sources), as well as the date the most recent search was executed.</td><td>6</td></tr><tr><td>Search</td><td>8</td><td>Present the full electronic search strategy for at least 1 database, including any limits used, such that it could be repeated.</td><td>6</td></tr><tr><td>Selection of sources of evidence†</td><td>9</td><td>State the process for selecting sources of evidence (i.e., screening and eligibility) included in the scoping review.</td><td>6-7</td></tr><tr><td>Data charting process‡</td><td>10</td><td>Describe the methods of charting data from the included sources of evidence (e.g., calibrated forms or forms that have been tested by the team before their use, and whether data charting was done independently or in duplicate) and any processes for obtaining and confirming data from investigators.</td><td>8-9</td></tr><tr><td>Data items</td><td>11</td><td>List and define all variables for which data were sought and any assumptions and simplifications made.</td><td>33</td></tr><tr><td>Critical appraisal of individual sources of evidence§</td><td>12</td><td>If done, provide a rationale for conducting a critical appraisal of included sources of evidence; describe the methods used and how this information was used in any data synthesis (if appropriate).</td><td>N/A</td></tr><tr><td>Synthesis of results</td><td>13</td><td>Describe the methods of handling and summarizing the data that were charted.</td><td>17</td></tr></tbody></table>

<figure><img src="image_8.png" /></figure>
Supplementary Material

<table><thead><tr><th>SECTION</th><th>ITEM</th><th>PRISMA-ScR CHECKLIST ITEM</th><th>REPORTED ON PAGE #</th></tr></thead><tbody><tr><td colspan="4"><strong>RESULTS</strong></td></tr><tr><td>Selection of sources of evidence</td><td>14</td><td>Give numbers of sources of evidence screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally using a flow diagram.</td><td>8</td></tr><tr><td>Characteristics of sources of evidence</td><td>15</td><td>For each source of evidence, present characteristics for which data were charted and provide the citations.</td><td>12-16</td></tr><tr><td>Critical appraisal within sources of evidence</td><td>16</td><td>If done, present data on critical appraisal of included sources of evidence (see item 12).</td><td>N/A</td></tr><tr><td>Results of individual sources of evidence</td><td>17</td><td>For each included source of evidence, present the relevant data that were charted that relate to the review questions and objectives.</td><td>17-28</td></tr><tr><td>Synthesis of results</td><td>18</td><td>Summarize and/or present the charting results as they relate to the review questions and objectives.</td><td>28-29</td></tr><tr><td colspan="4"><strong>DISCUSSION</strong></td></tr><tr><td>Summary of evidence</td><td>19</td><td>Summarize the main results (including an overview of concepts, themes, and types of evidence available), link to the review questions and objectives, and consider the relevance to key groups.</td><td>29-33</td></tr><tr><td>Limitations</td><td>20</td><td>Discuss the limitations of the scoping review process.</td><td>33</td></tr><tr><td>Conclusions</td><td>21</td><td>Provide a general interpretation of the results with respect to the review questions and objectives, as well as potential implications and/or next steps.</td><td>33-34</td></tr><tr><td colspan="4"><strong>FUNDING</strong></td></tr><tr><td>Funding</td><td>22</td><td>Describe sources of funding for the included sources of evidence, as well as sources of funding for the scoping review. Describe the role of the funders of the scoping review.</td><td>34</td></tr></tbody></table>

JBI = Joanna Briggs Institute; PRISMA-ScR = Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews.

* Where *sources of evidence* (see second footnote) are compiled from, such as bibliographic databases, social media platforms, and Web sites.

† A more inclusive/heterogeneous term used to account for the different types of evidence or data sources (e.g., quantitative and/or qualitative research, expert opinion, and policy documents) that may be eligible in a scoping review as opposed to only studies. This is not to be confused with *information sources* (see first footnote).

‡ The frameworks by Arksey and O'Malley (6) and Levac and colleagues (7) and the JBI guidance (4, 5) refer to the process of data extraction in a scoping review as data charting.

§ The process of systematically examining research evidence to assess its validity, results, and relevance before using it to inform a decision. This term is used for items 12 and 19 instead of "risk of bias" (which is more applicable to systematic reviews of interventions) to include and acknowledge the various sources of evidence that may be used in a scoping review (e.g., quantitative and/or qualitative research, expert opinion, and policy document).

From: Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA.ScR): Checklist and Explanation. Ann Intern Med. 2018;169:467-473. doi: 10.7326/M18-0850.

<figure><img src="image_9.png" /></figure>

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