MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysis

Anke Meyer-Baese; Ayhan Can Erdur; Benedikt Wiestler; Daniel Rueckert; Daniel Scholz; Jan C. Peeken; Viktoria Ehm

REVIEW 5 major objections 4 minor 1 cited by

MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysis

T0 review · 5 major / 4 minor · reviewed 2026-08-04 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper claims that adapting DINOv2 for multi-modal MRI—modality-aware patch embeddings, whole-sequence masking, and semi-supervised labels—yields external-test MCC 0.60 for glioma subtypes, 11.1% above supervised baselines.

desk verdict Useful multi-modal DINOv2 adaptation with honest ablations, but the abstract cherry-picks the supervised variant's number and the baselines are too weak to support 'state-of-the-art.' read the letter →

arxiv 2509.06617 v1 pith:VQBWHXFT submitted 2025-09-08 eess.IV cs.CV

Daniel Scholz , Ayhan Can Erdur , Viktoria Ehm , Anke Meyer-Baese , Jan C. Peeken , Daniel Rueckert , Benedikt Wiestler This is my paper

classification eess.IVcs.CV

keywords multi-modalmedicalimagingDINOv2gliomasubtypeclassificationMRIsemi-supervisedlearningmissingmodalityrobustnessvisionfoundationmodelmaskedimagemodeling

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

The paper tries to show that a vision foundation model pre-trained on natural images can be made to work for multi-modal medical imaging—where the same patient is imaged with several MRI sequences—by changing how patches are embedded, how masking is done, and how labels enter training. Applied to glioma subtype classification from four MRI sequences, the adapted model reaches a Matthews Correlation Coefficient of 0.60 on an external test set, which the authors place 11.1% above the supervised baselines they compare against. The practical stake is clinical: MRI sequences are often missing in routine care, labels are scarce, and large unlabeled scan archives exist but are unused by supervised methods. The paper's central wager is that all three problems can be handled inside one DINOv2-style pre-training loop rather than through a task-specific architecture.

What carries the argument

The central mechanism is a modified patch embedding: z_{i,m} = z_p + z_i + z_m, where z_p is the pre-trained patch projection, z_i is the pre-trained positional embedding applied separately within each modality, and z_m is a learnable modality token that identifies which MRI sequence a patch came from. This lets the frozen backbone see multi-modal data as interleaved single-modality images rather than as one stitched image. Two further mechanisms carry the method: full modality masking, which drops every patch of one sequence in the student input and forces the student to reconstruct the teacher's features from the remaining sequences, thereby learning cross-modality relationships; and a sem

What would settle it

Run an external evaluation in which every patient is classified from the full 3D volume rather than a single 96x96 axial middle slice, with molecular subtype labels as ground truth; if patient-level MCC does not track the reported slice-level 0.60, the gains are slice-level and may not transfer clinically.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that the gap between a uni-modal natural-image foundation model and multi-modal medical imaging is not architectural but representational. By replacing the single set of positional embeddings with per-modality positional embeddings plus a learnable modality embedding for each MRI sequence, the same pre-trained DINOv2 transformer can consume patches from several modalities without corrupting spatial or modality identity. Full-modality masking—dropping all patches of one sequence from the student while the teacher sees all—turns the masked-patch objective into a cross-modality prediction task, which is what gives robustness to missing sequences. Final

Load-bearing premise

That one 96x96 axial slice through the tumor's middle captures enough molecular-subtype information to classify a patient; if subtype-defining features live in other slices or in volumetric texture, the external-test gain may not represent patient-level diagnostic accuracy.

Editorial extensions

If this is right

If correct, any DINOv2 checkpoint can be adapted to a multi-modal medical task by changing the embedding head and masking schedule, without altering the transformer backbone.
Semi-supervised pre-training on unlabeled MRI archives should become a standard first step for classification tasks with few labels: the authors report it beats fully supervised fine-tuning.
The full-modality masking result implies models can be deployed in clinics where one sequence is missing, with a smaller accuracy drop than models trained on complete inputs only.
The ablation ordering implies future multi-modal adaptations should keep per-modality position encoding and modality tokens, since naive modality concatenation or RGB stacking actively hurts performance.

Reading between the lines

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

The single-slice evaluation design suggests the method's true ceiling may be higher under volumetric aggregation: a 3D or multi-slice version could combine the cross-modality robustness with spatial context, a direction the paper names but does not test.
A stratified missing-modality analysis by which sequence is absent would likely show that robustness is strongest when the missing sequence is redundant; if the most diagnostically informative sequence is missing, performance may not match the average reported here.
Because the semi-supervised gain is uneven across classes and worsens the rare oligodendroglioma F1 on the external set, class-balanced pseudo-labeling or oversampling of rare subtypes is a natural extension.
The modality-embedding idea is not MRI-specific; it should transfer to any multi-image modality stack (CT/PET, multiparametric imaging) and to settings where scans arrive at different times.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

Short version: the paper has a genuinely useful idea—add modality embeddings and modality-level masking to DINOv2 so it can consume multi-sequence MRI and keep working when a sequence is missing—and the external test on TCGA is a real step. But the abstract oversells it: the 0.60 MCC it quotes is from the purely supervised variant, and the semi-supervised variant that the paper actually promotes gets 0.57. The +11.1% is computed against ResNet34, which is not current state of the art for glioma subtyping. That gap between headline and table is the thing to fix.

What's new is the combination, not a new learning principle. The multi-modal patch embedding is simple and sensible: instead of stacking sequences as RGB channels or concatenating tokens with shared positional embeddings, they add a learnable modality vector and apply positional embeddings per modality. The full-modality masking is a straightforward way to force reliance on cross-modality redundancy. The ablation in Table 3 shows each component helping monotonically, which is the right kind of evidence. Releasing code is a plus.

The soft spots are claim discipline and evaluation breadth. The abstract picks the best-looking row; the semi-supervised variant, which is the third stated contribution, is weaker externally. No confidence intervals or multiple seeds for the 214-patient external set, so the 0.03–0.06 MCC differences could be noise. The baseline set is thin: ResNet34 and two DINOv2 configurations, not the current published methods for glioma subtype classification, so 'surpassing state-of-the-art' is not established. Also, evaluation uses a single 96x96 axial slice through the tumor. The paper doesn't justify why middle-slice appearance suffices for subtype, which is clinically questionable.

For a reader working on foundation models for medical imaging, the recipe is worth knowing and the ablations are clean. For a reader focused on clinical deployment, the evaluation is too coarse. The paper deserves a serious referee, not a desk reject, but it should be revised so the claims match the actual rows and the baselines include contemporary methods. Send it to review with that condition.

Referee Report

5 major / 4 minor

Summary. The paper proposes MM-DINOv2, an adaptation of the DINOv2 vision foundation model to multi-modal MRI. Three modifications are introduced: modality-specific patch embeddings (with per-modality positional embeddings), a full-modality masking strategy during the masked-image-modeling objective, and a semi-supervised extension that combines the DINOv2 image-level objective with available class labels. The method is evaluated on glioma subtype classification from four MRI sequences, using a large multi-institutional training set and an external TCGA test set. The paper reports an external MCC of 0.60 and a +11.1% improvement over a ResNet34 baseline, and claims robustness to missing MRI sequences.

Significance. If the reported results are representative, the core architectural idea of adding modality embeddings and full-modality masking to a frozen-pretrained DINOv2 is a useful and practical contribution for multi-modal medical imaging, and the external validation on TCGA is a strength. The paper also makes code publicly available, which supports reproducibility. However, several load-bearing claims are not supported by the reported numbers as written: the headline 0.60 external MCC belongs to the supervised variant, not the semi-supervised method that is the paper's main contribution, and the semi-supervised variant's external MCC is actually lower (0.57). The 'state-of-the-art' claim is also not backed by any current SOTA comparator. These issues need to be resolved before the paper's central claims can be accepted.

major comments (5)

[Abstract; §4.1, Table 1] The abstract's headline claim, 'our method achieves an MCC of 0.6 ... surpassing state-of-the-art supervised approaches by +11.1%,' is not supported by Table 1 for the method emphasized in the paper. The 0.60 external MCC is the fully supervised MM-DINOv2 row; the semi-supervised MM-DINOv2 row, which includes the paper's third contribution, reports 0.57. The +11.1% is exactly (0.60−0.54)/0.54 against ResNet34. If 'our method' refers to the semi-supervised framework, the relative gain is +5.6%, not +11.1%, and this gap is not statistically established. Please correct the abstract or explicitly identify which configuration is being reported.
[§4.1, Table 1; §5] The claim that semi-supervised learning improves performance is contradicted by the external results. Table 1 shows that semi-supervised MM-DINOv2 improves internal MCC from 0.68 to 0.74 but decreases external MCC from 0.60 to 0.57. The Conclusion states that 'semi-supervised learning ... outperformed supervised training' without acknowledging the external drop. This is a load-bearing issue for contribution 3. The authors should report the external numbers and discuss possible reasons (e.g., domain shift, class imbalance) or temper the claim.
[§4.1, Table 1] 'Surpassing state-of-the-art supervised approaches' is not established. The only supervised comparators are ResNet34 and two RGB-DINOv2 variants. No recent glioma-subtype classification method (e.g., the multi-task method in [28] or the imbalance-aware method in [24]) is included. Without these baselines, the claim of state-of-the-art performance is unjustified. Either add representative SOTA baselines evaluated on the same external set, or reframe the claim as an improvement over the selected baselines.
[§4.1–§4.3, Tables 1–3] No uncertainty quantification is provided. The paper uses a single data split and gives no confidence intervals, error bars, or significance tests for the external test set of 214 patients. The differences between key numbers (e.g., 0.60 vs 0.57) are small enough that they may be within sampling noise. Reporting multiple seeds or bootstrap confidence intervals is necessary to support the comparative claims.
[§4.1; §3.5] The comparison between supervised and semi-supervised settings is not apples-to-apples. Section 4.1 states that supervised training uses full fine-tuning, while the semi-supervised setting uses 'pre-training, followed by linear evaluation.' This protocol difference alone can explain part of the performance gap. Additionally, evaluation in Section 3.5 uses a single 96×96 axial middle slice, whereas tumor subtype-relevant heterogeneity is often volumetric. The authors should justify the single-slice evaluation or provide a patient-level aggregated evaluation, otherwise the reported MCC may not reflect diagnostic accuracy in practice.

minor comments (4)

[Section 3.1] The sentence 'These positional embeddings are learned during the DINOv2 pre-training and continuously optimized during the pre-training' contains a redundant phrase. Also, it should be clarified whether these embeddings are frozen or fine-tuned in the proposed adaptation.
[Section 4.2] The missing-sequence evaluation is described as 'one MRI sequence is randomly masked.' It is unclear whether this is a single random mask per test sample, whether results are averaged over multiple masks, and how the masked modality is handled at the input (e.g., zero-filled). Please provide these details.
[Table 1 caption] The caption mentions 'continuous pre-training' but the text in Section 4.1 refers to 'pre-training, followed by linear evaluation.' This inconsistency should be resolved so the reader knows exactly which training/evaluation protocol each table row uses.
[Section 3.5] The dataset description states that all images provide all four sequences, but the training set includes both labeled and unlabeled subjects. Please clarify how the unlabeled subset is defined and whether the same missing-sequence simulation is applied during training.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: external test set is held out; no prediction reduces to a fitted input or self-citation.

full rationale

The paper's derivation chain is self-contained with respect to circularity. The central results (Table 1) are evaluated on an external TCGA test set that is explicitly held out from all training and validation (Section 3.5: 'We hold out the TCGA dataset [3] for external testing and randomly split the remaining datasets in 70/10/20% for training, validation, and internal testing'). No parameter is fitted to the external test set, and the design choices (modality embeddings, full-modality masking, semi-supervised loss) are architectural and trained with standard supervised/self-supervised objectives. The semi-supervised extension is based on Fini et al. [9], an external method, not on the authors' own prior work. The only self-citation is [24], a related-work reference on imbalance-aware losses; it is not load-bearing for the method or the headline result. The abstract's '+11.1%' arithmetic is derived from Table 1 by comparing the supervised MM-DINOv2 external MCC (0.60) to the ResNet34 baseline (0.54); this is a reporting inconsistency about which configuration constitutes 'our method', not a circular reduction. Similarly, the absence of contemporary SOTA baselines and confidence intervals is a correctness/rigor concern, not a circularity concern. Overall, the derivation does not define its predictions in terms of its inputs, does not fit a parameter and then rename it as a prediction, and does not import a uniqueness result via self-citation. Therefore, no significant circularity is present.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The method introduces no new physical or conceptual entities. The free parameters are training hyperparameters and one data-selection threshold. The key assumptions are about transferability of DINOv2 features, pseudo-label reliability, and slice-level sufficiency.

free parameters (4)

semi_supervised_loss_weight = 2.0
Set by hand in Section 3.4; controls balance between SSL and supervised losses.
softmax_temperature = 0.1
Set by hand in Section 3.4; sharpens teacher prototype scores.
label_smoothing = 0.1
Set by hand in Section 3.4 for the supervised loss.
tumor_pixel_threshold = 500
Hand-chosen threshold in Section 3.5 for selecting training slices; affects data sampling.

assumptions (3)

domain assumption DINOv2's pre-trained representations from natural images transfer to medical MRI slices.
The method relies on DINOv2 as a starting point, cited to [20,22,1,25]; this transfer is the core hypothesis.
domain assumption The teacher network's prototype scores provide reliable pseudo-labels for unlabeled data.
Semi-supervised objective in Section 3.3 replaces teacher pseudo-labels with real labels when available, assuming they are otherwise useful.
domain assumption A single axial slice of the tumor is sufficient to classify glioma subtype.
Training and evaluation use 2D slices (Section 3.4, 3.5), ignoring volumetric context.

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Cite this review

Pith. "Pith review of MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysis." pith.science (2026). https://pith.science/paper/VQBWHXFT

@misc{pith2026250906617,
  author       = {Pith},
  title        = {Pith review of: MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VQBWHXFT}},
  note         = {Machine review of arXiv:2509.06617}
}

read the original abstract

Vision foundation models like DINOv2 demonstrate remarkable potential in medical imaging despite their origin in natural image domains. However, their design inherently works best for uni-modal image analysis, limiting their effectiveness for multi-modal imaging tasks that are common in many medical fields, such as neurology and oncology. While supervised models perform well in this setting, they fail to leverage unlabeled datasets and struggle with missing modalities, a frequent challenge in clinical settings. To bridge these gaps, we introduce MM-DINOv2, a novel and efficient framework that adapts the pre-trained vision foundation model DINOv2 for multi-modal medical imaging. Our approach incorporates multi-modal patch embeddings, enabling vision foundation models to effectively process multi-modal imaging data. To address missing modalities, we employ full-modality masking, which encourages the model to learn robust cross-modality relationships. Furthermore, we leverage semi-supervised learning to harness large unlabeled datasets, enhancing both the accuracy and reliability of medical predictions. Applied to glioma subtype classification from multi-sequence brain MRI, our method achieves a Matthews Correlation Coefficient (MCC) of 0.6 on an external test set, surpassing state-of-the-art supervised approaches by +11.1%. Our work establishes a scalable and robust solution for multi-modal medical imaging tasks, leveraging powerful vision foundation models pre-trained on natural images while addressing real-world clinical challenges such as missing data and limited annotations.

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Figures reproduced from arXiv: 2509.06617 by the authors.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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