AI RESEARCH
Unsupervised Adaptation from FDG to PSMA PET/CT for 3D Lesion Detection under Label Shift
arXiv CS.AI
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ArXi:2603.13666v1 Announce Type: cross 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