  We assessed the trustworthiness of two self-supervision pretrained
transformer models, Swin UNETR and SMIT, for fine-tuned lung (LC) tumor
segmentation using 670 CT and MRI scans. We measured segmentation accuracy on
two public 3D-CT datasets, robustness on CT scans of patients with COVID-19, CT
scans of patients with ovarian cancer and T2-weighted MRI of men with prostate
cancer, and zero-shot generalization of LC for T2-weighted MRIs. Both models
demonstrated high accuracy on in-distribution data (Dice 0.80 for SMIT and 0.78
for Swin UNETR). SMIT showed similar near-out-of-distribution performance on CT
scans (AUROC 89.85% vs. 89.19%) but significantly better
far-out-of-distribution accuracy on CT (AUROC 97.2% vs. 87.1%) and MRI (92.15%
vs. 73.8%). SMIT outperformed Swin UNETR in zero-shot segmentation on MRI (Dice
0.78 vs. 0.69). We expect these findings to guide the safe development and
deployment of current and future pretrained models in routine clinical use.
