Organ masks applied in feature space improve weakly supervised scan-level CT classification.

Automated analysis of computed tomography (CT) scans is an active area of medical imaging research. Classification tasks often rely on scan-level labels without providing spatial information. When using frozen encoders from pretrained models, such as foundation models, enforcing anatomical focus by input-space cropping can shift the input distribution and degrade performance. We investigated whether organ masks can provide scalable anatomical guidance for weakly supervised classification using a frozen self-supervised Swin Transformer. Two strategies were evaluated: input-space organ centering and feature-space cropping, which applies organ masks to intermediate feature maps before pooling. Across three datasets and seven binary tasks, feature-space cropping matched or improved performance relative to full-image baselines, whereas input-space centering showed task-dependent effects. Feature-space cropping achieved a pooled improvement of 0.018 (95% confidence interval: 0.009-0.028) in area under the receiver operating characteristic curve, with the largest gains for liver lesion and pericardial effusion classification. Feature-space cropping reduced embedding dimensionality without loss of performance while preserving input distribution. In an ablation experiment of input-space centering, tighter crops reduced performance, highlighting the importance of peri-organ context. These findings demonstrate that feature-level anatomical guidance offers an efficient strategy to improve weakly supervised CT classification without retraining the encoder.