A domain-adaptive deep contrastive network for magnetic resonance imaging-driven bladder cancer classification.

Abstract

Bladder cancer is one of the most prevalent malignancies of the urinary system and is associated with high morbidity and mortality. With advances in medical image analysis, deep learning has shown promise for automated bladder cancer classification using magnetic resonance imaging (MRI). However, clinical deployment remains challenging due to substantial inter-center distributional discrepancies and limited feature discriminability between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). To address these challenges, we propose a Domain-Adaptive Deep Contrastive Network (DADCNet) for MRI-based bladder cancer classification. The proposed framework jointly incorporates source- and target-domain samples during feature learning to obtain domain-invariant yet discriminative representations, thereby improving cross-center generalization. In addition, a deep contrastive learning strategy is introduced to enhance inter-class separability and intra-class compactness, leading to more robust classification. Experiments conducted on a multi-center bladder cancer MRI dataset demonstrate that DADCNet consistently outperforms existing convolutional neural network- and Transformer-based methods, achieving an accuracy of 0.955, an F1-score of 0.955, and an area under the curve of 0.991.