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arXiv Computer Vision By Mingxin Liu, Chengfei Cai, Anwen Lu, Pengbo Xu, Jun Li, Jinze Li, Depin Chen, Jun Xu

Synergistic Information Disentanglement for Omni-modal Slide Representation Learning in Computational Pathology

Read the original on arXiv Computer Vision →

The paper introduces Φ-Omni, a self‑supervised learning framework for computational pathology that disentangles synergistic information across histology, genomics, and clinical reports using Partial Information Decomposition. By employing a Synergistic Information Bottleneck and a ΦID objective, the method suppresses redundant signals while maximizing irreducible cross‑modal synergy, leading to improved few‑shot performance on breast and lung whole‑slide image datasets. The authors demonstrate that Φ-Omni outperforms both supervised and other SSL baselines on eight external tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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