Metraipolis › archive › dc_71e2ec540fc5

Federated Multi-Task Learning for Bladder Tumor Segmentation and MIBC Classification Using a Hybrid CNN-Transformer Architecture — arXiv:2608.30458

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.30458
License (as archived): CC0-1.0
Archived: 2026-10-17T09:59:30.591Z
SHA-256 of the archived text: aff28ba79a1d86cc447f2798c58743fb7c80682f44daf8eb52094dbd6e8b99ac
Stable URL of this page: https://metraipolis.com/documento/dc_71e2ec540fc5

How to cite

Federated Multi-Task Learning for Bladder Tumor Segmentation and MIBC Classification Using a Hybrid CNN-Transformer Architecture — arXiv:2608.30458 — arXiv (en), https://arxiv.org/abs/2608.30458 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_71e2ec540fc5 (Metraipolis archive dc_71e2ec540fc5, sha256 aff28ba79a1d86cc447f2798c58743fb7c80682f44daf8eb52094dbd6e8b99ac)

BibTeX

@misc{dc71e2ec540fc5,
  title = {Federated Multi-Task Learning for Bladder Tumor Segmentation and MIBC Classification Using a Hybrid CNN-Transformer Architecture — arXiv:2608.30458},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.30458},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_71e2ec540fc5; sha256 aff28ba79a1d86cc447f2798c58743fb7c80682f44daf8eb52094dbd6e8b99ac},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.30458. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Accurate bladder tumor segmentation and assessment of mus- cle invasion from T2-weighted MRI are important for treatment plan- ning, but developing robust models across institutions is challenging be- cause patient data cannot be centrally pooled and imaging characteristics vary across scanners and acquisition protocols. We propose a federated multi-task learning framework for joint bladder tumor segmentation and MIBC/NMIBC classification across four clinical centers. The proposed Swin Hybrid model combines a ResNet-34 branch for local texture and boundary information with a Swin-Tiny Transformer for global anatomi- cal context. A segmentation-guided classification mechanism further uses tumor localization information to support MIBC prediction. We also investigate several augmentation strategies under both centralized and federated training to improve robustness to multi-center variability. Ex- periments on the FedBCa dataset show that the Swin Hybrid provides the best overall balance between segmentation and classification among the evaluated architectures. Under federated training, Geo+Elastic aug- mentation achieved a DSC of 0.8100 and a patient-level AUC of 0.8931, yielding the highest combined score of 0.8474. These results demonstrate that joint segmentation and classification can be effectively performed across multiple institutions using federated training without centralizing patient data.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-01 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.30458 — 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 2026-10-17T09:28:48.145Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).