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

Source: [Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection under Label Shift](<https://www.sota2.com/research/paper/69b8dfe60636e5bf4afbe7ec>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-18T05:00:04.467Z

Authors: Xiaofeng Liu, Menghua Xia, Yanis Chemli, Georges El Fakhri, Chi Liu, Jinsong Ouyang

Published: 2026-03-14T00:25:29.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Prostate cancer lesion detection on Prostate cancer PET/CT (test)](<https://www.sota2.com/research/sota/prostate-cancer-lesion-detection-on-prostate-cancer-pet-ct-test.md>): Prostate cancer lesion detection; Prostate cancer PET/CT (test); Mean TP: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.13666>)

[PDF](<https://arxiv.org/pdf/2603.13666.pdf>)