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REVIEW 4 major objections 5 minor 188 references

Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This review systematically maps 86 foundation models and 161 datasets for brain imaging and identifies the leading models per task, the first consolidated survey of the field.

desk verdict A useful first map of brain imaging foundation models with an honest limitations section; the model rankings are illustrative rather than authoritative. read the letter →

arxiv 2506.13306 v1 pith:QNZDAAKG submitted 2025-06-16 eess.IV cs.CV

Salah Ghamizi , Georgia Kanli , Yu Deng , Magali Perquin , Olivier Keunen This is my paper · ORCID
classification eess.IVcs.CV
keywords brainimagingdeeplearningfoundationmodelscancerneurodegenerativediseasesneurovascularsystematicreviewbenchmarking
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims to be the first systematic review devoted entirely to foundation models for brain imaging, spanning 161 datasets and 86 model architectures. It maps the field's design choices, training strategies, and tasks, and then tries to identify which models lead on each major benchmark. The payoff for a reader is a consolidated atlas of a scattered literature: it shows where brain foundation models have concentrated effort (MRI-based cancer segmentation), where they have neglected available data (PET imaging, mental-health conditions), and where evaluation is too inconsistent to support confident comparisons. If the review is right, it gives both clinicians and machine learning researchers a shared starting point and a list of blind spots to fix.

What carries the argument

Two devices carry the analysis. First, a tournament graph of reported performance: a directed edge from model A to model B means B beat A in at least one publication, and 'green' nodes mark peer-reviewed models with over 50 citations that no surveyed approach has yet outperformed; this is what produces the lists of leading models. Second, benchmark tables that line up self-reported scores on the few common testbeds — BraTS for segmentation and VQA-RAD for question answering — where the review can compare variants side by side. The inclusion criterion that defines a foundation model (training on at least two imaging modalities, two organs, or two brain pathologies) is what lets the review sweep in the 86 architectures in the first place.

What would settle it

Re-run the models the review crowns as leaders (e.g., MoME, BrainSegFounder, Med-VLP, MUMC, RadFM) on one shared benchmark — BraTS 2021 for segmentation and VQA-RAD for question answering — under identical preprocessing and metrics; if the tournament order changes materially, the paper's 'best model' conclusions fail.

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Extended reading notes

Core claim

The central discovery the authors claim is that brain imaging foundation models now form a recognizable but uneven research field: 86 architectures, most built on a handful of backbones (SAM, CLIP, U-Net, BERT), with a burst of growth after 2023. On benchmarks, the review crowns specific leaders — MoME and BrainSegFounder on BraTS segmentation, Med-VLP and MUMC on VQA-RAD question answering, RadFM among generative multimodal systems — while cautioning that the comparisons inherit whatever datasets and metrics the original papers chose. It also documents structural problems: about six percent of 3D imaging studies are duplicates or derivatives, creating leakage risk; only six models address demographic bias; only seven include human evaluation; and pathology coverage in models diverges from both disease prevalence and dataset availability. The authors therefore frame the review as a snapshot and a call for standardized benchmarks, broader pathology coverage, and clinically grounded evaluation.

Load-bearing premise

The rankings assume that the numbers reported by different papers, measured on different dataset versions and with different metrics, can be compared side by side; the paper itself concedes in Section 8 that the synthesis relies entirely on published claims.

Editorial extensions

If this is right

  • Researchers can use the 161-dataset, 86-model atlas as an entry point when choosing backbones and benchmarks for a new brain imaging project.
  • The tournament singles out a short list of leaders (such as MoME and BrainSegFounder for segmentation, Med-VLP and MUMC for question answering) that new methods should be compared against.
  • The finding that about six percent of 3D imaging studies come from duplicate datasets warns that multi-dataset training should include explicit deduplication to avoid leakage.
  • The scarcity of standardized benchmarks, with no dataset used by more than five models outside the two dominant ones, implies that the field needs shared evaluation protocols before progress becomes measurable.
  • Because only seven of 86 models included any human expert evaluation, clinical translation claims rest on algorithmic metrics that may not reflect diagnostic utility.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A fair side-by-side re-evaluation on fixed splits could reshuffle the tournament leaders, since the ranking is built from heterogeneous self-reported numbers; the paper's method naturally extends into a living leaderboard.
  • The mismatch between pathology prevalence and model coverage suggests that future dataset contributions (e.g., PET and mental-health cohorts) may advance the field more than new architectures.
  • The bias-aware practices of the six models that address demographic balance provide a testable template: requiring stratified performance reporting for every brain FM would let the field verify fairness claims.
  • The inclusion criteria treat models trained on two modalities, organs, or pathologies as foundation models, so the review's 'foundation model' category spans very different scales; separating genuinely large pretrained systems from smaller multi-task models might change the conclusions.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This manuscript is a systematic literature review of foundation models (FMs) applied to brain imaging. Following a PRISMA-based protocol (Section 3), the authors screened 541 records from Semantic Scholar, Crossref, and PubMed, augmented the list via surveys and snowballing, and included 86 FM architectures and 161 brain imaging datasets in their analysis. The review maps general trends (publication timeline, venues, code availability), architectural choices (backbones, encoders), training strategies (contrastive/masked learning, adapters/LoRA, MoE), task and pathology coverage, and a dataset inventory with modality, license, and deduplication statistics. It also proposes a 'tournament' approach (Section 6.1) and benchmark tables (Tables 2-3) to identify the best-performing models per task, and discusses pitfalls including bias, pathology imbalance, and evaluation weaknesses. The central claims are that this is the first comprehensive curated review of brain-imaging FMs, that it systematically analyzes 161 datasets and 86 model architectures, and that it highlights the leading models for each task.

Significance. If the inventory and trend analysis are accurate, the paper provides a valuable consolidated map of an active, fast-moving field. The dataset analysis—covering deduplication, licensing, anonymization, modality and pathology distribution—is particularly useful, as is the model analysis of backbone reliance, parameter-efficient fine-tuning, and task coverage. The explicit search protocol and the public interactive atlas on Notion are strengths that support reproducibility and updateability. However, the leaderboard component is not yet methodologically reliable: the tournament and benchmark tables rank models across different BraTS versions, different metric types (ACC vs F1 vs BERT similarity), and different evaluation conditions, using self-reported numbers. The paper itself concedes this in Section 8 ('our synthesis relies entirely on the figures and claims reported in the reviewed literature'), yet the 'best model' statements remain a prominent contribution. The citation-based 'not outperformed' criterion in Section 6.1 is defensible only as a literature-status snapshot, not as a performance ranking.

major comments (4)
  1. [Section 6.2, Table 2] The statement 'The results show that the best performing models are MoME and BrainSegFounder' is inconsistent with the numbers in Table 2, where MAE-Seg Africa24 (92.90) and OBJ-SAM (91.90) report higher DICE scores than either MoME (92.10) or BrainSegFounder (91.15). The caveats listed in the text (missing contrasts, small training subsets, population shift) do not explain the omission of MAE-Seg and OBJ-SAM from the 'best' designation, nor is any explicit exclusion criterion given. Please specify the exact inclusion and exclusion rules for the leaderboard (e.g., benchmark version, full-contrast evaluation, metric type) and apply them consistently to all rows of the table.
  2. [Section 6.2, Table 3 and Section 6.1] The rankings mix incomparable quantities. In Table 3, most rows report Accuracy, but RadFM and Med-PaLMM report F1, Med-Flamingo reports a BERT similarity score, and LLaVA-Med's open-ended column is Recall; in Table 2, BraTS versions from 2013 through 2024 are combined, and the text notes that some DICE values were converted from AUC. Ranking these together and then asserting which model is 'best' (e.g., 'Med-VLP achieving the best accuracy', 'MUMC achieving the best performance', 'RadFM achieving the best score of 78.09') is not methodologically sound. Because the identification of leading models is a central contribution (Abstract, Section 1, Section 6), please either (a) restrict all leaderboard claims to same-version, same-metric comparisons, (b) present the tables without any cross-metric 'best' statements, or (c) add a sensitivity analysis that omits non-comparable entries and shows whether the rankings are stable.
  3. [Section 3.1] The text says 'We restrict our research to the last 5 years' for the database search on 18 February 2025, which would exclude papers from 2019. Yet Section 4.1 includes Genesis and Med3D, both published in 2019, as the first models in scope. This is presumably because the augmentation step (surveys and snowballing) was not time-restricted, but the manuscript does not say so. Please state explicitly that the five-year window applies only to the database query and not to the augmentation/snowballing step, and adjust the wording in Section 4.1 accordingly.
  4. [Section 8 and Section 3] The single-reviewer screening is disclosed in the Limitations section, but Section 3 claims adherence to PRISMA 2020, which recommends at least two independent reviewers for screening. This deviation is load-bearing for the reliability of the included-study list. Please move or repeat the disclosure in the Methodology section, describe any mitigation (e.g., verification of a random subset by a second author), and discuss the potential impact of screening bias on the final set of 86 included models.
minor comments (5)
  1. [Section 4.2] The sentence 'Among the 32 vision-language brain FMs included in our study, 34% are based on CLIP [167, 42, 86, 40, 17, 122, 118, 70]' is arithmetically inconsistent with the cited list, which contains 8 items: 8/32 is 25%, not 34%. Please correct this percentage and audit nearby percentages in Section 4 for consistency.
  2. [Figures 2 and 3] The captions for Fig. 2 ('Cumulative number of FMs publications over the years') and Fig. 3 ('Most cited FM over the time period') appear to be mismatched with their content: the text around Fig. 2 discusses cumulative publication counts, while Fig. 3 shows a task-distribution chart that resembles Fig. 7. Please verify all figure-caption pairings.
  3. [Section 3.3] The word 'interative' should be 'interactive'. In addition, the Notion links are hosted on a third-party platform; given the review's reproducibility claims, consider archiving the interactive atlas (e.g., in Zenodo or as a supplementary PDF) so the data behind Tables 1-3 remains accessible.
  4. [Section 6.2] The text states that DICE scores were 'converted from AUC if the original only uses AUC metrics', but no conversion formula or reference is provided. AUC-to-DICE conversion is not a standard, well-defined operation; please either justify it with a citation or remove the converted entries from the leaderboard.
  5. [Global] Several typos and grammatical slips should be corrected in a final pass: 'metholodgy' (Section 3), 'keywods' and 'thee' (Section 3.1), 'anomymization' (Sections 5.2 and 8), 'Traditionnaly' (Section 7.1), 'dependant' (Section 4.2), 'speciliaized' (Section 7.2), and 'demyelating' (Section 5.2).

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the review's rankings and counts are aggregations of external literature, not quantities derived from this paper's own inputs.

full rationale

This is a systematic review, not a derivation chain. Its central outputs are curated counts (161 datasets, 86 models) and qualitative/ranking summaries. The only formal equations reproduced in the paper—LoRA's Delta-W = BA (Eq. 1-2), MoE gating (Eq. 3-4), and TCT (Eq. 5)—are quoted from external prior work ([56], [77], [41]) and are not used to derive the review's conclusions. The 'best model' identifications in Section 6 are obtained by aggregating self-reported evaluation figures from the surveyed papers, as the paper openly states: 'In both cases, we are relying on the empirical evaluation in the respective works' (Section 6), and Section 8 concedes 'Our synthesis relies entirely on the figures and claims reported in the reviewed literature...'. This is an input-to-output summarization of external results, not a fit-then-predict loop, so no quantity is constructed from itself. Some background citations are self-citations (e.g., [15], [48], [64], [65], [72], [106], [112], [168]), but they support generic statements about the importance of brain imaging or about ML bias and are not load-bearing for the inclusion criteria, dataset inventory, or model rankings. No uniqueness theorem or author-derived constraint is invoked to force any choice. The paper's own limitations about metric incomparability and single-reviewer screening weaken the strength of the rankings, but those are evidence-quality and reproducibility concerns, not circularity. Therefore the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The review carries no fitted constants. Its conclusions rest on three assumptions: an inclusion rule for what counts as a brain FM, trust in self-reported performance numbers, and citation counts as a proxy for impact. All three are stated or acknowledged in the paper in Sections 2.3, 6, and 8, and all three limit how much of the central map should be taken at face value.

assumptions (3)
  • ad hoc to paper Definition of 'foundation model' as models trained on at least two imaging modalities, two organs, or two pathologies (Section 2.3)
    This inclusion rule determines the entire corpus of 86 models; it is broader than Bommasani's definition and excludes organ-specific single-pathology models the authors might consider non-foundational.
  • domain assumption Google Scholar citation counts reflect impact (Section 5.1, Table 1 footnote and Figure 3)
    Used to rank models and datasets; citation counts are noisy, time-dependent, and not a direct measure of scientific quality.
  • domain assumption Self-reported evaluation numbers in the surveyed papers are accurate and comparable (Section 6), even though Section 8 warns they may not be
    The tournament and benchmark tables are built by comparing metrics reported in different papers with different dataset versions and metrics, e.g., ACC vs F1 vs BERT score in Table 3.

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Cite this review

Pith. "Pith review of Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research." pith.science (2026). https://pith.science/paper/QNZDAAKG

@misc{pith2026250613306,
  author       = {Pith},
  title        = {Pith review of: Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QNZDAAKG}},
  note         = {Machine review of arXiv:2506.13306}
}
read the original abstract

Foundation models (FMs), large neural networks pretrained on extensive and diverse datasets, have revolutionized artificial intelligence and shown significant promise in medical imaging by enabling robust performance with limited labeled data. Although numerous surveys have reviewed the application of FM in healthcare care, brain imaging remains underrepresented, despite its critical role in the diagnosis and treatment of neurological diseases using modalities such as MRI, CT, and PET. Existing reviews either marginalize brain imaging or lack depth on the unique challenges and requirements of FM in this domain, such as multimodal data integration, support for diverse clinical tasks, and handling of heterogeneous, fragmented datasets. To address this gap, we present the first comprehensive and curated review of FMs for brain imaging. We systematically analyze 161 brain imaging datasets and 86 FM architectures, providing information on key design choices, training paradigms, and optimizations driving recent advances. Our review highlights the leading models for various brain imaging tasks, summarizes their innovations, and critically examines current limitations and blind spots in the literature. We conclude by outlining future research directions to advance FM applications in brain imaging, with the aim of fostering progress in both clinical and research settings.

Figures

Figures reproduced from arXiv: 2506.13306 by the authors.

Figure 1
Figure 1. Prisma Methodology followed in our SLR. The databases and registries are Scholar, Crossref, and PubMed; The other methods include [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Cumulative number of FMs publications over the years [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Most cited FM over the time period while the most cited publication in a conference tops at 698 citations (avg 84.03, median 37.5), and the most cited publication in a journal reaches 1433 citations (avg 131.88, median 11). The relative similarity of metrics between venues hints that very high impact in terms of citations can be achieved undifferentiable through jour￾nals, conferences, and preprints, which suggests … view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Distribution of venues ML; 19 Medical Imaging; 17 Health; 6 Computer Vision 8 General; 34 [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Distribution of communities license [4], 16 under the Apache 2.0 license [1], three under the GNU GPL license [3], and three un￾der Creative Commons Attribution Noncommercial li￾censes [2]. Lava-Med is under the Microsoft Research License that restricts commercial appl…
Figure 6
Figure 6. Figure 6: Mindmap of Training Techniques of FMs were published along models, and the count may re￾flect the interest of both the dataset and the model (on 01/04/2025). 9/15 of the 2d imaging datasets are multimodal datasets, with medical images and associated text data: figure c…
Figure 7
Figure 7. Figure 7: Distribution of tasks. The dashed tasks with red borders are [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Distribution of pathologies in the Foundation Models. [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 10
Figure 10. Figure 10: Number of datasets per imaging modality and sequences. [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 9
Figure 9. Figure 9: Mindmap of 3D Brain MRI datasets split by pathologies. In parentheses the total number of studies of the datasets [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 11
Figure 11. Figure 11: Best Multimodal Foundation Models by tournament. In orange and green are the FM models covered, and in green are the best. A [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: Best 2D vision-only foundation models by tournament. In orange and green are the FM models covered, and in green are the best. A [PITH_FULL_IMAGE:figures/full_fig_p015_12.png]
Figure 13
Figure 13. Figure 13: Best 3D vision-only foundation models by tournament. In orange and green are the FM models covered, and in green are the best. A [PITH_FULL_IMAGE:figures/full_fig_p016_13.png]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.