Multiparametric magnetic resonance imaging (mpMRI) has demonstrated promising
results in prostate cancer (PCa) detection using deep convolutional neural
networks (CNNs). Recently, transformers have achieved competitive performance
compared to CNNs in computer vision. Large-scale transformers need abundant
annotated data for training, which are difficult to obtain in medical imaging.
Self-supervised learning can effectively leverage unlabeled data to extract
useful semantic representations without annotation and its associated costs.
This can improve model performance on downstream tasks with limited labelled
data and increase generalizability. We introduce a novel end-to-end
Cross-Shaped windows (CSwin) transformer UNet model, CSwin UNet, to detect
clinically significant prostate cancer (csPCa) in prostate bi-parametric MR
imaging (bpMRI) and demonstrate the effectiveness of our proposed
self-supervised pre-training framework. Using a large prostate bpMRI dataset
with 1500 patients, we first pre-train CSwin transformer using multi-task
self-supervised learning to improve data-efficiency and network
generalizability. We then finetuned using lesion annotations to perform csPCa
detection. Five-fold cross validation shows that self-supervised CSwin UNet
achieves 0.888 AUC and 0.545 Average Precision (AP), significantly
outperforming four state-of-the-art models (Swin UNETR, DynUNet, Attention
UNet, UNet). Using a separate bpMRI dataset with 158 patients, we evaluated our
model robustness to external hold-out data. Self-supervised CSwin UNet achieves
0.79 AUC and 0.45 AP, still outperforming all other comparable methods and
demonstrating generalization to a dataset shift.

本研究通过引入自监督预训练框架，提出了一种全新的端到端 Cross-Shaped windows (CSwin) transformer UNet 模型，用于检测临床重要的前列腺癌症，通过在大量未标记的数据上进行学习，提高数据效率和网络的泛化能力，成功地提高了模型性能。