Metraipolis › archive › dc_bcb0ca5ce1cc

Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillation — arXiv:2609.37682

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.37682
License (as archived): CC0-1.0
Archived: 2026-10-07T18:41:27.658Z
SHA-256 of the archived text: 1cad128b71f7d9aa918532bb711531166d7a052c6500bbda4a88211b785f5a6d
Stable URL of this page: https://metraipolis.com/documento/dc_bcb0ca5ce1cc

How to cite

Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillation — arXiv:2609.37682 — arXiv (en), https://arxiv.org/abs/2609.37682 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bcb0ca5ce1cc (Metraipolis archive dc_bcb0ca5ce1cc, sha256 1cad128b71f7d9aa918532bb711531166d7a052c6500bbda4a88211b785f5a6d)

BibTeX

@misc{dcbcb0ca5ce1cc,
  title = {Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillation — arXiv:2609.37682},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.37682},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bcb0ca5ce1cc; sha256 1cad128b71f7d9aa918532bb711531166d7a052c6500bbda4a88211b785f5a6d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.37682. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The rapid expansion of large-scale medical datasets and computational resources has driven significant progress in medical foundation models. Given the inherent heterogeneity of medical imaging modalities, current research mainly follows two paths: specialized models optimized for specific modalities, and generalist models designed to handle multiple modalities. However, medical generalist models suffer from both insufficient training data scale relative to natural image generalists and inadequate domain-specific depth relative to medical specialists. Empirically, generalist models establish a cross-modality performance baseline, while specialists define the performance ceiling within their respective domains. To elevate this baseline toward these ceilings, we propose Med-RADIO, a medical multi-teacher distillation framework that Reduces All Domains Into One by compressing complementary expertise from multiple domain-specific teachers into a unified medical vision foundation model. Our method curates both generalist and specialist teachers, allocates modality-aligned distillation streams to reorganize generalist pretraining data so it matches specialist domains, and uses a balanced loss to prevent any single teacher from dominating the distillation process. On internal and external classification benchmarks spanning five modalities, Med-RADIO improves over strong medical generalists under linear probing and remains competitive with representative specialists on most evaluated modalities. Code is available at this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-30 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.37682 — 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 2026-10-07T18:04:24.899Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).