Self-supervised learning (SSL) is an approach to extract useful feature
representations from unlabeled data, and enable fine-tuning on downstream tasks
with limited labeled examples. Self-pretraining is a SSL approach that uses the
curated task dataset for both pretraining the networks and fine-tuning them.
Availability of large, diverse, and uncurated public medical image sets
provides the opportunity to apply SSL in the "wild" and potentially extract
features robust to imaging variations. However, the benefit of wild- vs
self-pretraining has not been studied for medical image analysis. In this
paper, we compare robustness of wild versus self-pretrained transformer (vision
transformer [ViT] and hierarchical shifted window [Swin]) models to computed
tomography (CT) imaging differences for non-small cell lung cancer (NSCLC)
segmentation. Wild-pretrained Swin models outperformed self-pretrained Swin for
the various imaging acquisitions. ViT resulted in similar accuracy for both
wild- and self-pretrained models. Masked image prediction pretext task that
forces networks to learn the local structure resulted in higher accuracy
compared to contrastive task that models global image information.
Wild-pretrained models resulted in higher feature reuse at the lower level
layers and feature differentiation close to output layer after fine-tuning.
Hence, we conclude: Wild-pretrained networks were more robust to analyzed CT
imaging differences for lung tumor segmentation than self-pretrained methods.
Swin architecture benefited from such pretraining more than ViT.

自我监督学习（SSL）是一种从无标签数据中提取有用特征表示并在有限标记样本的下游任务中进行精细调整的方法。本论文比较了基于自然数据和自我预训练的转换器模型在医学图像分析中的鲁棒性，发现自然预训练模型对肺肿瘤分割的 CT 图像差异具有更高的适应性，同时 Swin 架构从这种预训练中受益更多。

自我监督学习提高深度学习对 CT 图像差异的肺肿瘤分割的鲁棒性

Self-supervised learning improves robustness of deep learning lung tumor  segmentation to CT imaging differences

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.

基于 670 个 CT 和 MRI 扫描，评估了两个自监督预训练的 Transformer 模型 Swin UNETR 和 SMIT 在肺肿瘤细分领域的可信度。模型表现出了较高的准确性和稳健性，并在不同领域的 CT 和 MRI 扫描中展现了良好的分割性能。研究结果有望在临床中指导当前和未来预训练模型的安全开发和应用。