Pith Number

pith:TJK5NSPP

pith:2026:TJK5NSPP4LUJCPVVURKEMR26OY

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Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data

Brandon Bujak, Charidimos Tsagkas, Daniel Reich, Govind Nair, Irene Cortese, Julien Cohen-Adad, Kuan Yi Wang, Paul Hoareau, Roy Sun

2D and 3D segmentation models require distinct regularization when trained from sparse 2D MRI annotations.

arxiv:2605.12753 v1 · 2026-05-12 · eess.IV · cs.CV · cs.LG

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The 2D Teacher required strong spatial augmentation and soft-labeling to overcome data scarcity, improving White Matter Lesion Dice scores by >11 points. However, propagating these techniques to the 3D Student degraded its performance. Furthermore, human-centric preprocessing (e.g., CLAHE) disrupted global statistical cues, dropping Gray Matter Lesion Dice scores by ~25 points.

C2weakest assumption

That the pseudo-labels generated by the 2D teacher model are accurate enough to serve as reliable training targets for the 3D student without introducing systematic errors that explain the observed performance differences.

C3one line summary

Sparse-to-dense 3D segmentation from 2D slices shows divergent regularization needs: 2D benefits from strong augmentation and soft labels while 3D does not, and human-centric preprocessing harms performance.

References

24 extracted · 24 resolved · 0 Pith anchors

[1] Image Augmentation Techniques for Mammogram Analysis

[2] Yoshimi, Yuki and Mine, Yuichi and Ito, Shota and Takeda, Saori and Okazaki, Shota and Nakamoto, Takashi and Nagasaki, Toshikazu and Kakimoto, Naoya and Murayama, Takeshi and Tanimoto, Kotaro. Image p

[3] ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

[4] Contrastive learning of global and local features for medical image segmentation with limited annotations

[5] Grey matter pathology in multiple sclerosis

Receipt and verification

First computed	2026-05-18T03:09:48.679742Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9a55d6c9efe2e8913eb5a45446475e761fb8d97b6e221583977c9d8b9443063d

Aliases

arxiv: 2605.12753 · arxiv_version: 2605.12753v1 · doi: 10.48550/arxiv.2605.12753 · pith_short_12: TJK5NSPP4LUJ · pith_short_16: TJK5NSPP4LUJCPVV · pith_short_8: TJK5NSPP

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TJK5NSPP4LUJCPVVURKEMR26OY \
  | 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: 9a55d6c9efe2e8913eb5a45446475e761fb8d97b6e221583977c9d8b9443063d

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "3d76a28a9f3c1cb0801af6cef2eadb0a7dc0e932b43781ac0125e60219814a0d",
    "cross_cats_sorted": [
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.IV",
    "submitted_at": "2026-05-12T21:06:53Z",
    "title_canon_sha256": "0a20724d1e6a6e05df63deb052698c048c275ebc0ca841b14f6f124addc22279"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12753",
    "kind": "arxiv",
    "version": 1
  }
}