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Anatomy-Aware Promptable Segmentation with Online Interactive Training for AUTOPET V

Paper recorded by Signals 4 on 2026-08-28 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-28 on arXiv · recorded by Signals 4 on 2026-08-31

Category: cs.AI · 人工智能 · first seen 2026-08-31

Abstract

We present an anatomy-aware, promptable model for whole-body lesion segmentation in FDG and PSMA PET/CT, developed for the AUTOPET V challenge. The proposed method is built as family of nnU-Net-based models and trained in two stages: i) a pre-training stage that produces a strong initial segmentation, and ii) an online interactive stage that learns to exploit scribble prompts, refining the predict

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#299 most recent of 300 cs.AI papers we have recorded · ↑ newer: Real-time virtual circuits for plasma shape control via neural network · ↓ older: ARC-CT: Anatomy-Routed Contrastive Vision-Language Learning for 3D Che

Cite this page: Anatomy-Aware Promptable Segmentation with Online Interactive Training for AUTOPET V: the #299 most recent of 300 cs.AI papers we have recorded (as of 2026-08-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/anatomy-aware-promptable-segmentation-with-online-interactive-training-for-autop.html
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