Hybrid Transformer Model with Augmentation for Kidney Tumor Segmentation

Precise segmentation of kidney tumors in medical images is crucial for diagnosis, treatment planning, and prognosis assessment. In this work, we present a newly proposed hybrid deep learning model that combines the merits of U-Net and the Swin Transformer architectures in order to enhance the segmentation performance. Although U-Net has great spatial localization ability thanks to the encoder–decoder structure, which works in a hierarchical way, it is still difficult to capture global context well. The Swin Transformer instead captures long-range dependencies and assists in local detail extraction, while attention pooling might also smear fine boundary details. This motivates our hybrid integration. To attempt to resolve these issues, we extend U-Net with the Swin Transformer blocks in the backbone encoder path in order to efficiently perform multi-scale semantic feature extraction while preserving structural consistency. We trained and cross-validated the model on the publicly available Kidney Tumor Segmentation Challenge 2021 (KiTS21) dataset with extensive data augmentation as well as custom loss functions to address class imbalance and boundary obscureness. Experiments demonstrated that it achieved better performance when compared with the solo models, seeking a similar multi-task learning objective on not only U-Net and the Swin Transformer but also other baseline architectures in terms of the average Dice similarity coefficient (average DSC), intersection over union score (IoU) and Hausdorff distance. The proposed model achieved a Dice similarity coefficient (DSC) of 0.91, an IoU of 0.87, a PR-AUC of 0.89, and an overall voxel-wise accuracy of 98%, demonstrating robust and precise kidney tumor segmentation across varying tumor sizes and shapes. Moreover, the integrated solution is more robust and generalizes better, particularly in challenging cases with diverse anatomical variations. These findings demonstrate the power of Transformer-based hybrid models for medical image segmentation. Our results have positive implications for the design of computer-aided diagnostic systems and their association with other prevalent medical imaging tasks besides organ-specific or pathology-focused tasks.