%\section{Conclusion}

In conclusion, the U-Mamba architecture, with its enhanced long-range dependency modeling, improved PCa detection in bpMRI. Additionally, we demonstrated that integrating zonal masks via multi-task learning further enhanced PCa detection performance.

% In conclusion, the enhanced long-range dependency capabilities of the U-Mamba architecture positively influenced prostate cancer (PCa) segmentation, as evidenced by its superior performance with large input sizes and improved ability to capture global contextual information. Additionally, the integration of zonal masks into the U-Mamba architecture via multi-task learning demonstrated improvements in tumor localization and segmentation performance, particularly in the in-house dataset and the PI-CAI Development set, although with some variability in results across datasets. These findings highlight the potential of leveraging long-range dependency modeling and auxiliary tasks like zonal segmentation to enhance PCa detection and segmentation.