Ultrasound-based dominant intraprostatic lesion classification with swin transformer

In prostate brachytherapy, focal boost on dominant intraprostatic lesions (DILs) can reduce the recurrence rate while keeping low toxicity. In recent years, ultrasound (US) prostate tissue characterization has demonstrated the feasibility in detecting dominant intraprostatic lesions. With recent developments in computer-aided diagnosis (CAD), deep learningbased methods have provided solutions for efficient analysis of US images. In this study, we aim to develop a Shiftedwindows (Swin) Transformer-based method for DIL classification. The self-attention layers in Swin Transformer allow efficient feature discrimination between benign tissues and intraprostatic lesions. We simplified the structure of Swin Transformer to avoid overfitting on a small dataset. The proposed transformer structure achieved 83% accuracy and 0.86 AUC at patient level on three-fold cross validation, demonstrating the feasibility of applying our method for dominant lesion classification from US images, which is of clinical significance for radiotherapy treatment planning.