Cross-Domain Transfer in Residual Networks for Clinical Image Partitioning

The accurate recognition and comprehensive understanding of medical images depicting human tissue represent a central focus in computer vision research. Many tasks within medical imaging rely on deep neural networks, particularly those with U-shaped architectures and skip connections. The advancement of computer vision technologies demands the application of convolutional neural networks (CNNs). Despite progress, two major challenges remain in medical image processing: (1) developing a model framework with low computational complexity that allows for efficient inference without compromising accuracy, and (2) designing a model with strong generalization capabilities across various datasets derived from patients with differing pathologies, thereby mitigating domain shift challenges. In response to the first issue, we propose a novel unsupervised domain adaptation method utilizing Interoperable Batch Normalization (IBN) to integrate multiple channels within deep neural networks, enhancing adversarial domain adaptation. Our experimental evaluation on the Hubmap and Synapse multiorgan segmentation datasets reveals that the proposed RRUNet model achieves superior performance compared to existing methods, setting a new standard in the domain.