Random forest-based out-of-distribution detection for robust lung cancer segmentation

Accurate detection and segmentation of cancerous lesions from computed tomography (CT) scans is essential for automated treatment planning and cancer treatment response assessment. Transformer-based models with self-supervised pretraining have achieved strong performance on in-distribution (ID) data but often generalize poorly on out-of-distribution (OOD) inputs. We investigate this behavior for lung cancer segmentation using an encoder-decoder model. Our encoder is a Swin Transformer pretrained with masked image modeling (SimMIM) on 10,432 unlabeled 3D CT scans spanning cancerous and non-cancerous conditions, and the decoder was randomly initialized. This model was evaluated on an independent ID test set and four OOD scenarios, including chest CT cohorts (pulmonary embolism and negative COVID-19) and abdomen CT cohorts (kidney cancers and non-cancerous pancreas). OOD detection was performed at the scan level using RF-Deep, a random forest classifier applied to contextual tumor-anchored feature representations. We evaluated 920 3D CTs (172,650 images) and observed that RF-Deep achieved FPR95 values of 18.26% and 27.66% on the chest CT cohorts, and near-perfect detection (less than 0.1% FPR95) on the abdomen CT cohorts, consistently outperforming established OOD methods. These results demonstrate that our RF-Deep classifier provides a simple, lightweight, and effective approach for enhancing the reliability of segmentation models in clinical deployment.