Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection under Label Shift
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 stabili...
| Authors | Xiaofeng Liu, Menghua Xia, Yanis Chemli et al. |
|---|---|
| Primary category | eess.IV |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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