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Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V — arXiv:2608.30844

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.30844
License (as archived): CC0-1.0
Archived: 2026-10-17T07:55:01.220Z
SHA-256 of the archived text: 9a8368c31ae7e9f9162fe150f555469c9ee6e1514016b6e2557e4c6a68a527d4
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How to cite

Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V — arXiv:2608.30844 — arXiv (en), https://arxiv.org/abs/2608.30844 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_208cd4e648e6 (Metraipolis archive dc_208cd4e648e6, sha256 9a8368c31ae7e9f9162fe150f555469c9ee6e1514016b6e2557e4c6a68a527d4)

BibTeX

@misc{dc208cd4e648e6,
  title = {Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V — arXiv:2608.30844},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.30844},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_208cd4e648e6; sha256 9a8368c31ae7e9f9162fe150f555469c9ee6e1514016b6e2557e4c6a68a527d4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.30844. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Interactive lesion segmentation in whole-body PET/CT requires a model to provide a strong initial prediction while also responding efficiently to sparse corrective scribbles during inference. This setting is particularly challenging because tracer distributions, physiological uptake patterns, lesion appearance, and acquisition characteristics differ substantially between FDG and PSMA studies. We present TRIAGE, Tracer-aware Refinement via Interactive Anatomy-Guided sEgmentation. The core backbone is a 3D STU-Net initialized through masked autoencoding pre-training with an asynchronous masking strategy, aiming to learn transferable anatomical and cross-modal representations before task-specific fine-tuning. In parallel, we train an auxiliary organ segmentation model whose predictions provide explicit anatomical context and help distinguish physiological uptake from malignant lesions. A dedicated tracer classifier first routes each study to an FDG- or PSMA-specific branch. Within each branch, a first-stage segmentation model consumes CT, PET, and organ context to generate an initial lesion mask. The initial prediction is then combined with cumulative foreground/background scribbles and refined by a second interactive segmentation network. The FDG and PSMA branches share the same overall processing pipeline but are trained independently to account for tracer-specific appearance and error modes. We additionally employ curriculum-style training and model ensembling to improve robustness across interaction steps and heterogeneous cohorts. Experiments are conducted using the official AutoPET V data and ten-fold split; quantitative results, ablations, and final test-set performance are left as placeholders to be completed after the challenge evaluation. Code: this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-01 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.30844 — 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 2026-10-17T07:28:26.914Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).