  Biparametric magnetic resonance imaging (bpMRI) has demonstrated promising
results in prostate cancer (PCa) detection using 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 (SSL) utilizes unlabeled data to generate meaningful
semantic representations without the need for costly annotations, enhancing
model performance on tasks with limited labeled data. 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 pretrain CSwin transformer using multi-task
self-supervised learning to improve data-efficiency and network
generalizability. We then finetune 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 comparable models (Swin UNETR, DynUNet, Attention UNet,
UNet). Using a separate bpMRI dataset with 158 patients, we evaluate our method
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 good generalization to external data.
