Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection under Label Shift

TL;DR

This paper introduces an unsupervised domain adaptation framework for 3D lesion detection that effectively handles label shift between FDG PET/CT and PSMA PET/CT scans, improving detection performance.

Contribution

It proposes novel self-training mechanisms that explicitly model and compensate for label shift, including adaptive anchor shape adjustment and size bin-wise pseudo-label quotas.

Findings

Improved AP and FROC scores on AutoPET 2024 dataset.

Effective handling of label shift in cross-tracer lesion detection.

Outperforms baseline and conventional self-training methods.

Abstract

In this work, we propose an unsupervised domain adaptation (UDA) framework for 3D volumetric lesion detection that adapts a detector trained on labeled FDG PET/CT to unlabeled PSMA PET/CT. Beyond covariate shift, cross tracer adaptation also exhibits label shift in both lesion size composition and the number of lesions per subject. We introduce self-training with two mechanisms that explicitly model and compensate for this label shift. First, we adaptively adjust the detection anchor shapes by re-estimating target domain box scales from selected pseudo labels and updating anchors with an exponential moving average. This increases positive anchor coverage for small PSMA lesions and stabilizes box regression. Second, instead of a fixed confidence threshold for pseudo-label selection, we allocate size bin-wise quotas according to the estimated target domain histogram over lesion volumes. The self-training alternates between supervised learning with prior-guided pseudo labeling on PSMA and supervised learning on labeled FDG. On AutoPET 2024, adapting from 501 labeled FDG studies to 369 $^{18}$F-PSMA studies, the proposed method improves both AP and FROC over the source-only baseline and conventional self-training without label-shift mitigation, indicating that modeling target lesion prevalence and size composition is an effective path to robust cross-tracer detection.