Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation

TL;DR

RF-Deep is a lightweight, post-hoc random forests framework that enhances out-of-distribution detection in lung tumor segmentation, using limited labeled data and deep features from pretrained models.

Contribution

Introduces RF-Deep, a novel post-hoc OOD detection method leveraging deep features with minimal labeled data, improving clinical safety in tumor segmentation.

Findings

Achieved AUROC > 93 on near-OOD datasets.

Near-perfect detection with AUROC > 99 on far-OOD datasets.

Demonstrated transferability to blinded validation datasets.

Abstract

Accurate segmentation of lung tumors from 3D computed tomography (CT) scans is essential for automated treatment planning and response assessment. Despite self-supervised pretraining on numerous datasets, state-of-the-art transformer backbones remain susceptible to out-of-distribution (OOD) inputs, often producing confidently incorrect segmentations with potential for risk in clinical deployment. Hence, we introduce RF-Deep, a lightweight post-hoc random forests-based framework that leverages deep features trained with limited outlier exposure, requiring as few as 40 labeled scans (20 in-distribution and 20 OOD), to improve scan-level OOD detection. RF-Deep repurposes the hierarchical features from the pretrained-then-finetuned segmentation backbones, aggregating features from multiple regions-of-interest anchored to predicted tumor regions to capture OOD likelihood. We evaluated RF-Deep on 2,232 CT volumes spanning near-OOD (pulmonary embolism, COVID-19 negative) and far-OOD (kidney cancer, healthy pancreas) datasets. RF-Deep achieved AUROC >~93 on the challenging near-OOD datasets, where it outperformed the next best method by 4--7 percentage points, and produced near-perfect detection (AUROC >~99) on far-OOD datasets. The approach also showed transferability to two blinded validation datasets under the ensemble configuration (COVID-19 positive and breast cancer; AUROC >~94). RF-Deep maintained consistent performance across backbones of different depths and pretraining strategies, demonstrating applicability of post-hoc detectors as a safety filter for clinical deployment of tumor segmentation pipelines.