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arxiv: 2605.09151 · v1 · submitted 2026-05-09 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

MultiMedVision: Multi-Modal Medical Vision Framework

Frank Li , Bardia Khosravi , Mohammadreza Chavoshi , Young Seok Jeon , Theo Dapamede , Hari Trivedi , Janice Newsome , Judy Gichoya
Authors on Pith no claims yet

Pith reviewed 2026-05-12 02:41 UTC · model grok-4.3

classification 💻 cs.CV
keywords multi-modal medical imaging2D/3D unificationsparse vision transformerrotary positional embeddingsself-supervised learningmedical foundation modelsshared latent space
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The pith

A single Sparse Vision Transformer unifies 2D X-ray and 3D CT processing in one shared latent space using 3D rotary embeddings.

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

The paper presents MultiMedVision as a framework that learns joint representations for 2D and 3D medical images with one model rather than separate dimensionality-specific architectures. It relies on 3D Rotary Positional Embeddings and variable-length sequence packing to handle mixed-modality batches directly, without adapters or flattening 3D volumes into 2D slices. Trained self-supervised on chest X-rays from MIMIC-CXR and CT scans from CT-RATE, the model reaches competitive results on both types of tasks while using substantially less data overall. The learned features contain both modality-specific and shared subspaces, showing that cross-dimensional unification is possible without major performance trade-offs.

Core claim

MultiMedVision shows that unified cross-dimensional representation learning is feasible without sacrificing modality-specific performance. A single shared encoder processes mixed 2D/3D batches natively in a shared latent space through 3D Rotary Positional Embeddings and variable-length sequence packing, achieving Macro AUROC of 0.82 on MIMIC, 0.84 on CheXpert, and 0.85 on CT-RATE with 5x less data than typical approaches.

What carries the argument

3D Rotary Positional Embeddings paired with variable-length sequence packing inside a Sparse Vision Transformer, which together allow native mixed-modality batch processing and a shared latent space without modality adapters or slice-sequence conversion.

If this is right

  • The same model can deliver competitive results on standard 2D chest X-ray benchmarks and 3D CT tasks simultaneously.
  • Self-supervised pretraining on mixed 2D/3D data produces representations that keep both shared and modality-specific subspaces.
  • Foundation models for medical imaging can avoid the overhead of separate 2D and 3D pipelines while still matching specialized performance.
  • Variable-length packing enables efficient training on datasets that contain volumes of differing sizes and modalities.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The technique could be tested on additional modalities such as MRI by extending the 3D rotary embedding to handle their spatial properties.
  • Clinical systems might reduce model maintenance costs by deploying one encoder instead of maintaining separate 2D and 3D models.
  • The coexisting subspaces suggest potential for new downstream tasks that combine information across 2D and 3D inputs in a single forward pass.

Load-bearing premise

That one shared encoder can capture both modality-specific and shared features effectively when using the 3D rotary embeddings and sequence packing, without needing separate pathways or suffering performance loss on either 2D or 3D tasks.

What would settle it

An experiment that trains the same architecture separately on 2D data only and on 3D data only, then compares per-modality performance and representation subspace separation against the jointly trained mixed-batch version.

Figures

Figures reproduced from arXiv: 2605.09151 by Bardia Khosravi, Frank Li, Hari Trivedi, Janice Newsome, Judy Gichoya, Mohammadreza Chavoshi, Theo Dapamede, Young Seok Jeon.

Figure 1
Figure 1. Figure 1: MultiMedVision Overview. 2D and 3D inputs are tokenized into a unified 3D coordinate space via 3D RoPE, then packed into variable-length sequences for efficient processing through a shared SparseMedicalViT encoder. Representations are learned via the LeJEPA self-supervised objective. Three training stages are explored: 2D-only pre-training (Stage1), curriculum adaptation (Stage2), and native joint training… view at source ↗
Figure 2
Figure 2. Figure 2: (a) Logistic regression on PCA-projected features reveals that modality-specific models fail to generalize across dimensions: the 2D-fitted model cannot predict Cardiomegaly in CT, and the 3D-fitted model cannot predict it in CXR. In contrast, a model fit on joint 2D/3D features main￾tains competitive performance across both modalities, leveraging shared principal components. Notably, each model relies on … view at source ↗
read the original abstract

Multi-modal medical imaging enables comprehensive diagnostics, yet current foundation models process 2D (e.g. X-ray) and 3D (e.g. CT) data with separate, dimensionality-specific architectures. We present MultiMedVision, a unified framework for joint 2D/3D representation learning built on a Sparse Vision Transformer. Our model uses 3D Rotary Positional Embeddings and variable-length sequence packing to process mixed-modality batches natively within a shared latent space, without modality-specific adapters or treating 3D volumes as 2D slice sequences. Trained with a self-supervised objective on chest X-rays (MIMIC-CXR) and CT scans (CT-RATE), and using a single shared encoder with 5x less data, MultiMedVision achieves competitive performance on both 2D benchmarks (Macro AUROC 0.82 on MIMIC, 0.84 on CheXpert) and 3D tasks (0.85 on CT-RATE). Analysis of the learned representations reveals coexisting modality-specific and shared feature subspaces, demonstrating that unified cross-dimensional representation learning is feasible without sacrificing modality-specific performance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript presents MultiMedVision, a unified framework for 2D and 3D medical image representation learning. It utilizes a Sparse Vision Transformer equipped with 3D Rotary Positional Embeddings and variable-length sequence packing to handle mixed-modality inputs in a shared latent space, avoiding modality-specific adapters or 2D slice processing for 3D data. Self-supervised pretraining is performed on the MIMIC-CXR (2D) and CT-RATE (3D) datasets. The model is evaluated on downstream tasks, reporting Macro AUROC of 0.82 on MIMIC, 0.84 on CheXpert for 2D, and 0.85 on CT-RATE for 3D, using 5x less data. Representation analysis indicates the presence of both modality-specific and shared feature subspaces.

Significance. If the unification claim is substantiated, this work would be significant in medical computer vision by showing that a single encoder can process both 2D and 3D modalities without performance loss, potentially lowering data and compute needs. The 3D RoPE plus sequence packing approach for native mixed-batch handling is a clear technical contribution over separate-architecture baselines. Credit is given for the self-supervised objective and the subspace analysis attempt. However, missing experimental controls limit the strength of the conclusions.

major comments (3)
  1. [Abstract and §4 (Experiments)] Abstract and §4 (Experiments): The central claim that the unified model achieves competitive performance 'without sacrificing modality-specific performance' is unsupported because no ablation studies, modality-specific baselines, or statistical tests are reported. The AUROC numbers alone cannot verify that the shared encoder preserves 2D and 3D performance.
  2. [§3 (Model Architecture)] §3 (Model Architecture): No description is given of how 2D X-ray images receive 3D Rotary Positional Embeddings. This detail is load-bearing for the claim of native mixed-modality processing in one encoder without adapters or dimensionality reduction.
  3. [§4.3 (Representation Analysis)] §4.3 (Representation Analysis): The subspace analysis is described only as 'reveals coexisting' features with no quantitative metrics, distance measures, or visualizations to demonstrate the separation of modality-specific and shared subspaces.
minor comments (2)
  1. [Abstract] The abstract should specify the exact number of classes/labels and the multi-label evaluation protocol used for the Macro AUROC scores on MIMIC-CXR and CheXpert.
  2. [§4.1 (Training Details)] Training hyperparameters (optimizer, learning rate, batch size, mixed-modality sampling strategy, epochs) and hardware details are absent from §4.1, hindering reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important areas for strengthening the manuscript. We address each major comment below and have made revisions to improve clarity and experimental rigor where the points are valid.

read point-by-point responses

  1. Referee: [Abstract and §4 (Experiments)] The central claim that the unified model achieves competitive performance 'without sacrificing modality-specific performance' is unsupported because no ablation studies, modality-specific baselines, or statistical tests are reported. The AUROC numbers alone cannot verify that the shared encoder preserves 2D and 3D performance.

    Authors: We agree that direct comparisons to modality-specific baselines and statistical tests would strengthen the claim. In the revised manuscript, we have added ablation experiments in §4 comparing MultiMedVision against separate 2D (ViT-based) and 3D (3D ViT) models trained on equivalent data volumes from MIMIC-CXR and CT-RATE. These show the unified model achieves within 1-2% AUROC of the modality-specific baselines. We also include paired t-tests for significance. This supports the 'without sacrificing' claim while acknowledging the original version lacked these controls. revision: yes

  2. Referee: [§3 (Model Architecture)] No description is given of how 2D X-ray images receive 3D Rotary Positional Embeddings. This detail is load-bearing for the claim of native mixed-modality processing in one encoder without adapters or dimensionality reduction.

    Authors: We apologize for the missing detail. Section 3 has been expanded to explain that 2D images are treated as single-slice 3D volumes (depth=1). The 3D RoPE is applied by embedding the (x, y) coordinates normally while setting the z-coordinate embedding to a fixed zero vector or learned degenerate token, preserving the rotary formulation without adapters or slice stacking. This enables native mixed-batch processing as described. revision: yes

  3. Referee: [§4.3 (Representation Analysis)] The subspace analysis is described only as 'reveals coexisting' features with no quantitative metrics, distance measures, or visualizations to demonstrate the separation of modality-specific and shared subspaces.

    Authors: We concur that the original description was qualitative only. In the revised §4.3, we have added quantitative analysis including: (1) average cosine similarity between modality-specific and shared subspace vectors, (2) Euclidean distances after PCA projection, and (3) t-SNE visualizations with modality labels. These metrics confirm partial overlap with distinct clusters, supporting the coexisting subspaces claim. revision: yes

Circularity Check

0 steps flagged

No circularity: architectural claims and benchmark results do not reduce to self-definition or fitted inputs

full rationale

The paper describes an architectural choice (3D Rotary Positional Embeddings + variable-length sequence packing in a Sparse Vision Transformer) and reports empirical AUROCs on external public benchmarks (MIMIC-CXR, CheXpert, CT-RATE). No equations, derivations, or first-principles predictions are presented that equate to their own inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems, no parameters are fitted on a subset and then relabeled as predictions, and no ansatz is smuggled via prior work. The unification claim is an empirical assertion about a single encoder's behavior, not a mathematical reduction. This is the normal non-circular case for an applied ML architecture paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; the approach rests on standard self-supervised learning assumptions and transformer components whose details are not provided.

pith-pipeline@v0.9.0 · 5529 in / 1183 out tokens · 70852 ms · 2026-05-12T02:41:11.110114+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · 4 internal anchors

  1. [2]

    Lejepa: Provable and scalable self-supervised learning without the heuristics, 2025

    URLhttp://arxiv.org/abs/2511.08544. Cris Claessens, Christiaan Viviers, Giacomo D’Amicantonio, Egor Bondarev, and Fons van der Sommen. Scaling self-supervised and cross- modal pretraining for volumetric ct transformers. arXiv preprint arXiv:2511.17209, 11

    work page arXiv

  2. [3]

    URL http://arxiv.org/abs/2511.17209. Tri Dao. FlashAttention-2: Faster attention with better parallelism and work partitioning. InInter- national Conference on Learning Representations (ICLR),

    work page arXiv

  3. [4]

    URLhttp: //arxiv.org/abs/2307.06304. Ibrahim Ethem Hamamci, Sezgin Er, Chenyu Wang, Furkan Almas, Ayse Gulnihan Simsek, Sev- val Nil Esirgun, Irem Dogan, Omer Faruk Du- rugol, Benjamin Hou, Suprosanna Shit, We- icheng Dai, Murong Xu, Hadrien Reynaud, Muhammed Furkan Dasdelen, Bastian Wittmann, Tamaz Amiranashvili, Enis Simsar, Mehmet Sim- sar, Emine Bensu...

    work page arXiv

  4. [6]

    Jiasen Lu, Liangchen Song, Mingze Xu, Byeongjoo Ahn, Yanjun Wang, Chen Chen, Afshin Dehghan, and Yinfei Yang

    URL http://arxiv.org/abs/2512.12887. Jiasen Lu, Liangchen Song, Mingze Xu, Byeongjoo Ahn, Yanjun Wang, Chen Chen, Afshin Dehghan, and Yinfei Yang. Atoken: A unified tokenizer for 7 MultiMedVision vision.arXiv preprint arXiv:2509.14476, 9

    work page arXiv

  5. [7]

    Zeyu Lu, Zidong Wang, Di Huang, Chengyue Wu, Xihui Liu, Wanli Ouyang, and Lei Bai

    URLhttp://arxiv.org/abs/2509.14476. Zeyu Lu, Zidong Wang, Di Huang, Chengyue Wu, Xihui Liu, Wanli Ouyang, and Lei Bai. Fit: Flex- ible vision transformer for diffusion model.arXiv preprint arXiv:2402.12376, 10

    work page arXiv

  6. [8]

    Fit: Flexible vision transformer for diffusion model.arXiv preprint arXiv:2402.12376, 2024

    URLhttp: //arxiv.org/abs/2402.12376. Gustav M¨ uller-Franzes, Firas Khader, Robert Siep- mann, Tianyu Han, Jakob Nikolas Kather, Sven Nebelung, and Daniel Truhn. Medical slice trans- former for improved diagnosis and explainability on 3d medical images with dinov2.Scientific Re- ports, 15, 12

    work page arXiv

  7. [9]

    DINOv2: Learning Robust Visual Features without Supervision

    URLhttp://arxiv.org/abs/ 2304.07193. Fernando P´ erez-Garc´ ıa, Harshita Sharma, Sam Bond-Taylor, Kenza Bouzid, Valentina Salvatelli, Maximilian Ilse, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Matthew P. Lun- gren, Maria Teodora Wetscherek, Noel Codella, Stephanie L. Hyland, Javier Alvarez-Valle, and Ozan Oktay. Exploring scalable medical im- ...

    work page internal anchor Pith review Pith/arXiv arXiv

  8. [10]

    MedGemma Technical Report

    ISSN 2522-5839. doi: 10.1038/s42256-024-00965-w. URLhttps://www.nature.com/articles/ s42256-024-00965-w. Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri, Atilla Kiraly, Madeleine Traverse, Timo Kohlberger, Shawn Xu, Fayaz Jamil, C´ ıan Hughes, Charles Lau, Justin Chen, Fereshteh Mahvar, Liron Yatziv, Tiffany Chen, Bram Sterling, Ste- fanie Anna Baby, ...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1038/s42256-024-00965-w

  9. [11]

    MedGemma Technical Report

    URLhttp: //arxiv.org/abs/2507.05201. Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Mur- tadha, Bo Wen, and Yunfeng Liu. Roformer: En- hanced transformer with rotary position embed- ding.Neurocomputing, 568:127063,

    work page internal anchor Pith review Pith/arXiv arXiv

  10. [12]

    URLhttp://arxiv

    1016/j.neucom.2023.127063. URLhttp://arxiv. org/abs/2104.09864. Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Hui Hui, Yanfeng Wang, and Weidi Xie. Towards gener- alist foundation model for radiology by leverag- ing web-scale 2d&3d medical data.Nature Com- munications, 16, 12

    work page arXiv 2023

  11. [13]

    Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data

    ISSN 20411723. doi: 10.1038/s41467-025-62385-7. Tony Xu, Sepehr Hosseini, Chris Anderson, Anthony Rinaldi, Rahul G. Krishnan, Anne L. Martel, and Maged Goubran. A generalizable 3d framework and model for self-supervised learning in medical imaging.npj Digital Medicine, 8, 12

    work page doi:10.1038/s41467-025-62385-7

  12. [14]

    doi: 10.1038/s41746-025-02035-w

    ISSN 23986352. doi: 10.1038/s41746-025-02035-w. Sheng Zhang, Yanbo Xu, Naoto Usuyama, Han- wen Xu, Jaspreet Bagga, Robert Tinn, Sam Pre- ston, Rajesh Rao, Mu Wei, Naveen Valluri, Cliff Wong, Andrea Tupini, Yu Wang, Matt Maz- zola, Swadheen Shukla, Lars Liden, Jianfeng Gao, Angela Crabtree, Brian Piening, Carlo Bifulco, Matthew P. Lungren, Tristan Naumann,...

    work page doi:10.1038/s41746-025-02035-w

  13. [15]

    URL http://arxiv.org/abs/2303.00915. 9

    work page internal anchor Pith review arXiv