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MiTHras: Task-specific Hierarchical Semi-supervised Contrastive Masked Autoencoder for Mitotic Figure Analysis — arXiv:2609.24736

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.24736
License (as archived): CC0-1.0
Archived: 2026-10-09T07:08:52.050Z
SHA-256 of the archived text: 55a4013ea879f4befef641b4569495cc507950ba2e6515600cd339535a17c413
Stable URL of this page: https://metraipolis.com/documento/dc_2bbceacb074d

How to cite

MiTHras: Task-specific Hierarchical Semi-supervised Contrastive Masked Autoencoder for Mitotic Figure Analysis — arXiv:2609.24736 — arXiv (en), https://arxiv.org/abs/2609.24736 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2bbceacb074d (Metraipolis archive dc_2bbceacb074d, sha256 55a4013ea879f4befef641b4569495cc507950ba2e6515600cd339535a17c413)

BibTeX

@misc{dc2bbceacb074d,
  title = {MiTHras: Task-specific Hierarchical Semi-supervised Contrastive Masked Autoencoder for Mitotic Figure Analysis — arXiv:2609.24736},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.24736},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2bbceacb074d; sha256 55a4013ea879f4befef641b4569495cc507950ba2e6515600cd339535a17c413},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.24736. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Mitotic figure (MF) analysis supports tumor grading and prognostic assessment, but automated models remain sensitive to differences in tissue type and image acquisition. We present MiTHras, a task-specific pretraining framework that combines pseudo-label-guided image- and token-level contrastive learning with masked reconstruction. We construct TCGA-MF-Pseudo, a corpus of 1.8 million cell-centered images from 14 TCGA cohorts spanning 11 organ sites. Comprehensive evaluation on MF classification, detection, count-based survival prediction, and subtype classification demonstrates the efficacy of MiTHras. It achieves the highest mean F1 on all three MF classification benchmarks and both subtype benchmarks. MiTHras also outperforms general-purpose and pathology foundation encoders by a larger margin under frozen-encoder linear probing than under full fine-tuning. Although detection gains are modest due to a shared candidate-detection stage, ablations confirm that token-level supervision improves typical-versus-atypical classification and linear probing. These findings establish that MiTHras yields robust, transferable representations for automated mitotic activity assessment.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-22 in cs.LG).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.24736 — 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 2026-10-09T06:58:09.659Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).