Ultrasound-based dominant intraprostatic lesion classification with Swin Transformer

Abstract

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.

Synthetic CT generation from MRI using 3D diffusion model

“…Traditional methods in synthesizing CT from MRI, including atlas-and segmentation-based techniques [3,[5][6][7], often encounter accuracy limitations stemming from registration or segmentation errors. These technological advancements have exhibited considerable promise in tasks based on MRI and CT encompassing segmentation, classification, and the recognition of text-based information [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Furthermore, they have achieved state-of-the-art performance in the synthesis of MRI and CT [23][24][25][26][27].…”