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pith:6EHDR6IZ

pith:2026:6EHDR6IZELD7BOYYKI64LROOOU

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VolTA-3D: Self-Supervised Learning for Brain MRI using 3D Volumetric Token Alignment

Abhijeet Parida, Amy Makawana, Julia Ive, Marius George Linguraru, Syed Muhammad Anwar

VolTA-3D aligns global class-style tokens and local patch tokens in a student-teacher setup to learn transferable 3D representations from unlabeled brain MRI.

arxiv:2605.16775 v1 · 2026-05-16 · cs.CV · cs.AI · cs.LG

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Claims

C1strongest claim

Hence jointly enforcing global semantic consistency and local structural learning during pretraining enables broader concept learning from unlabeled brain MRI data. Overall VolTA-3D supports effective multi-task downstream performance with task-specific pretraining, a step towards generalizable and clinically viable 3D models.

C2weakest assumption

The premise that the limited semantic diversity and subtle anatomical characteristics of brain MRI specifically challenge existing SSL approaches and that the proposed global-local token alignment within a student-teacher paradigm will overcome these challenges to produce improved transferability and robustness under domain shift.

C3one line summary

VolTA-3D learns transferable 3D representations from unlabeled brain MRI by jointly aligning global and local tokens in a self-supervised student-teacher framework.

References

23 extracted · 23 resolved · 1 Pith anchors

[1] Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence, 2021

[2] Mri seg- mentation of the human brain: Challenges, methods, and applications, 2015

[3] Building a general simclr self-supervised foundation model across neurological diseases to advance 3d brain mri diagnoses, 2025

[4] Domain adaptation for medical image analysis: A survey, 2022

[5] Comparing 3d, 2.5 d, and 2d approaches to brain image auto-segmentation, 2023

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Receipt and verification

First computed	2026-05-20T00:03:21.342709Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f10e38f91922c7f0bb18523dc5c5ce7510545c21e1259786aa0595bec76d5a79

Aliases

arxiv: 2605.16775 · arxiv_version: 2605.16775v1 · doi: 10.48550/arxiv.2605.16775 · pith_short_12: 6EHDR6IZELD7 · pith_short_16: 6EHDR6IZELD7BOYY · pith_short_8: 6EHDR6IZ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/6EHDR6IZELD7BOYYKI64LROOOU \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f10e38f91922c7f0bb18523dc5c5ce7510545c21e1259786aa0595bec76d5a79

Canonical record JSON

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    "abstract_canon_sha256": "732e438c0cdce275f0c84d82977a54a1a1c1da7fdef5231000ccb29e7321e1f8",
    "cross_cats_sorted": [
      "cs.AI",
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    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-16T03:09:25Z",
    "title_canon_sha256": "317aeb0c0fa03a8db06f88f2b6bd5bf78b417c759c4e8b06b14113201728b8eb"
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