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Beyond the Dominant Label: Recovering Coexisting Lesion Evidence with Multiple Instance Learning — arXiv:2610.22091

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.22091
License (as archived): CC0-1.0
Archived: 2026-10-16T10:25:21.635Z
SHA-256 of the archived text: e7c5a37fb861eb1327c237512a8cd6f6bfd843f2ac48fdd8af1d6715566e1833
Stable URL of this page: https://metraipolis.com/documento/dc_ffa77df3e170

How to cite

Beyond the Dominant Label: Recovering Coexisting Lesion Evidence with Multiple Instance Learning — arXiv:2610.22091 — arXiv (en), https://arxiv.org/abs/2610.22091 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ffa77df3e170 (Metraipolis archive dc_ffa77df3e170, sha256 e7c5a37fb861eb1327c237512a8cd6f6bfd843f2ac48fdd8af1d6715566e1833)

BibTeX

@misc{dcffa77df3e170,
  title = {Beyond the Dominant Label: Recovering Coexisting Lesion Evidence with Multiple Instance Learning — arXiv:2610.22091},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.22091},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ffa77df3e170; sha256 e7c5a37fb861eb1327c237512a8cd6f6bfd843f2ac48fdd8af1d6715566e1833},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.22091. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Whole-slide images may contain coexisting lesions, yet weak supervision typically provides only the most severe diagnosis. We formulate this setting as dominant-label multiple instance learning, where the observed label is the maximum-severity element of a latent lesion set. Standard multiclass MIL treats it as a complete one-hot description and therefore suppresses lower-severity evidence whenever it co-occurs with a dominant lesion. We propose DomSet-MIL, a label-aware framework that aligns the annotation mechanism across bag and instance levels. Dominant-Label Censored Likelihood models the reported diagnosis without declaring unreported lesions absent. Severity-Ordered Residual Routing assigns patch evidence from high to low severity and preserves residual support for secondary findings, while top-$q$ pooling links local routing scores to bag prediction. Experiments across natural and controlled settings show that DomSet-MIL achieves state-of-the-art performance in dominant-label prediction while more effectively recovering latent coexisting-lesion evidence. These findings demonstrate that modeling the dominant-label mechanism can improve diagnostic prediction without sacrificing clinically meaningful secondary findings.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.22091 — 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 2026-10-16T08:48:02.939Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).