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Training-Time Domain Transfer for Generator-Free Diagnostics on Novel Modalities in Computational Pathology — arXiv:2611.02931

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02931
License (as archived): CC0-1.0
Archived: 2026-11-03T10:58:46.686Z
SHA-256 of the archived text: 9dab8f9ef3017178ab666ba8a3821edd13bdcf10e2f07e374464a4b9d99b76ab
Stable URL of this page: https://metraipolis.com/documento/dc_abe2c7ddb024

How to cite

Training-Time Domain Transfer for Generator-Free Diagnostics on Novel Modalities in Computational Pathology — arXiv:2611.02931 — arXiv (en), https://arxiv.org/abs/2611.02931 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_abe2c7ddb024 (Metraipolis archive dc_abe2c7ddb024, sha256 9dab8f9ef3017178ab666ba8a3821edd13bdcf10e2f07e374464a4b9d99b76ab)

BibTeX

@misc{dcabe2c7ddb024,
  title = {Training-Time Domain Transfer for Generator-Free Diagnostics on Novel Modalities in Computational Pathology — arXiv:2611.02931},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02931},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_abe2c7ddb024; sha256 9dab8f9ef3017178ab666ba8a3821edd13bdcf10e2f07e374464a4b9d99b76ab},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02931. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Novel imaging modalities promise improved diagnostics, but AI development is constrained by scarce training data. Inference-time domain transfer can address this by mapping novel modalities to established ones, enabling reuse of existing diagnostic models. However, this introduces hallucination risks, computational overhead, and regulatory complexity. Inspired by sim-to-real transfer, we propose training-time domain transfer, generating synthetic target-domain training data from archived routine samples to enable direct diagnosis on real inputs without inference-time generation. Using unstained prostate histopathology as a proof-of-concept, we compare this approach with inference-time virtual staining and conventional H&E-based cancer detection. Training-time transfer achieved higher concordance with the conventional benchmark than virtual staining for both raw (Cohen's $\kappa$=0.59 vs. 0.37; Gwet's AC1=0.73 vs. 0.29) and deparaffinized tissue ($\kappa$=0.66 vs. 0.61; AC1=0.73 vs. 0.64), supporting its potential for emerging diagnostic modalities.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.02931 — 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 2026-11-03T10:49:38.696Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).