Trustworthiness of Pretrained Transformers for Lung Cancer Segmentation
- URL: http://arxiv.org/abs/2403.13113v1
- Date: Tue, 19 Mar 2024 19:36:48 GMT
- Title: Trustworthiness of Pretrained Transformers for Lung Cancer Segmentation
- Authors: Aneesh Rangnekar, Nishant Nadkarni, Jue Jiang, Harini Veeraraghavan,
- Abstract summary: Two self-supervision transformer models, Swin UNETR and SMIT, were tested for fine-tuned lung tumor segmentation.
Both models demonstrated high accuracy on in-distribution data.
We expect these findings to guide the safe development and deployment of current and future pretrained models.
- Score: 6.712251433139412
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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