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MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities — arXiv:2608.19788

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.19788
License (as archived): CC0-1.0
Archived: 2026-10-18T12:04:59.345Z
SHA-256 of the archived text: afb31a54332de5936a00698b6b9fc649830946daa86e98ee2f3f22aa20b637f5
Stable URL of this page: https://metraipolis.com/documento/dc_ab320cd9526c

How to cite

MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities — arXiv:2608.19788 — arXiv (en), https://arxiv.org/abs/2608.19788 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ab320cd9526c (Metraipolis archive dc_ab320cd9526c, sha256 afb31a54332de5936a00698b6b9fc649830946daa86e98ee2f3f22aa20b637f5)

BibTeX

@misc{dcab320cd9526c,
  title = {MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities — arXiv:2608.19788},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.19788},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ab320cd9526c; sha256 afb31a54332de5936a00698b6b9fc649830946daa86e98ee2f3f22aa20b637f5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.19788. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Trustworthy multimodal fusion in clinical settings requires handling incomplete and heterogeneous modality subsets across institutions, where privacy constraints prohibit centralized data sharing. Federated learning (FL) mitigates data-sharing constraints but suffers from client-specific missing modalities, where institutions possess incomplete multimodal subsets, degrading fusion quality and segmentation performance. While FL and weak supervision have been studied separately, their joint use with image-level labels under heterogeneous missing modalities remains unaddressed. We propose \textbf{MOSAIC}, the first modality-agnostic federated framework for weakly supervised binary tumor segmentation under client-specific missing modalities. We introduce a client-specific modality-alignment module that fuses available channels into a shared latent space without prior knowledge of modality identity, a spectral prototype alignment loss that reconciles cross-client distribution shift using compact non-invertible frequency-domain statistics, and a dedicated federated refinement network that denoises the resulting CAM pseudo-labels into accurate masks, breaking the accuracy ceiling of weak supervision. Experiments on three multi-institutional brain tumor benchmarks (FeTS2022, BraTS-MEN, and BraTS-SSA) demonstrate significant improvements over all image, box, and point-supervised baselines, approaching fully supervised accuracy using only image-level labels and reaching 0.84 Dice on FeTS2022. Dynamic new client addition enables previously unseen institutions to join an already-trained federation within 0.01-0.04 Dice without retraining. Code is available at this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-21 in cs.CV).
Categorie arXiv: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.19788 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T12:01:57.766Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).