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

Research Medical/Healthcare AI

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

Merged summary

TL;DR - MOSAIC is a federated framework for weakly supervised tumor segmentation when hospitals have different missing imaging modalities. It approaches fully supervised performance using only image-level labels while preserving decentralized data.

  • A modality-agnostic alignment module maps each client’s available imaging channels into a shared latent space without knowing modality identities.
  • Spectral prototype alignment uses compact, non-invertible frequency-domain statistics to reduce cross-client distribution shift.
  • A federated refinement network converts noisy class activation map pseudo-labels into more accurate segmentation masks.
  • Across three brain tumor benchmarks, MOSAIC outperformed image-, box-, and point-supervised baselines, reaching 0.84 Dice on FeTS2022; new institutions joined without federation-wide retraining and performed within 0.01–0.04 Dice.

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

arXiv eess.IV Tarun Kumar Garg, Vaanathi Sundaresan 2026-08-20 arXiv:2608.19788
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-19 14:25:04.360513 UTC

TL;DR - MOSAIC is a federated framework for weakly supervised tumor segmentation when hospitals have different missing imaging modalities. It approaches fully supervised performance using only image-level labels while preserving decentralized data.

  • A modality-agnostic alignment module maps each client’s available imaging channels into a shared latent space without knowing modality identities.
  • Spectral prototype alignment uses compact, non-invertible frequency-domain statistics to reduce cross-client distribution shift.
  • A federated refinement network converts noisy class activation map pseudo-labels into more accurate segmentation masks.
  • Across three brain tumor benchmarks, MOSAIC outperformed image-, box-, and point-supervised baselines, reaching 0.84 Dice on FeTS2022; new institutions joined without federation-wide retraining and performed within 0.01–0.04 Dice.
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