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arXiv Computer Vision By Libo Zhang

Three-Phase Scribble-Adaptive Curriculum Learning for autoPETV Grand Challenge

Read the original on arXiv Computer Vision →

The paper presents Libo Zhang’s algorithmic solution for the autoPETV Grand Challenge, focusing on interactive lesion segmentation in whole-body PET/CT scans. The method encodes user scribbles as two additional input channels and trains a large residual‑encoder U‑Net (≈140 M parameters) through a three‑phase curriculum over 4000 epochs, progressively moving from fully automatic segmentation to handling user‑provided scribbles and finally correcting its own mistakes via simulated error‑driven steps. Using 1811 studies for training and an ensemble of five‑fold checkpoints, the approach achieves a mean AUC‑Dice of 3.836 and a mean AUC‑DMM of 3.869 in interactive five‑fold cross‑validation, with significant gains from the first corrective scribble.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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