# Weakly Supervised Pathology Localization

Localizing pathology in 3D CT normally requires experts to outline lesions voxel by voxel, which is costly and hard to extend across findings. Uni-Loc replaces the classification head with similarity scores between a pathology-conditioned query and spatial image features, so the same scores yield both a scan-level diagnosis and a 3D localization map, with gradient-guided thresholding outlining lesion regions. Across five localization tasks covering 355 annotated CT scans, it reaches macro-averaged voxel-level AUROC/AUPR of 96.1%/34.5%, against 80.7%/13.9% for relevance propagation and 88.7%/8.1% for gradient saliency.

Localizing pathology in 3D CT normally requires experts to outline lesions voxel by voxel, which is costly and hard to extend across findings. Uni-Loc replaces the classification head with similarity scores between a pathology-conditioned query and spatial image features, so the same scores yield both a scan-level diagnosis and a 3D localization map, with gradient-guided thresholding outlining lesion regions. Across five localization tasks covering 355 annotated CT scans, it reaches macro-averaged voxel-level AUROC/AUPR of 96.1%/34.5%, against 80.7%/13.9% for relevance propagation and 88.7%/8.1% for gradient saliency.