\begin{abstract}




Prostate cancer (PCa) remains a leading cause of cancer-related morbidity, emphasizing the need for accurate and non-invasive diagnostic tools. While deep learning models have advanced PCa detection in magnetic resonance imaging (MRI), they often fail to integrate anatomical knowledge. This study evaluates U-Mamba, a deep learning architecture designed to enhance long-range dependency modeling with linear time complexity, for PCa detection. Furthermore, a multi-task learning (MTL) extension, U-Mamba MTL, is introduced to incorporate prostate zonal anatomy, aligning with clinical diagnostic workflows. The models were assessed using diverse datasets, including the PI-CAI hidden tuning cohort (N=100) and an in-house collected out-of-distribution cohort (N=200). Results demonstrate that U-Mamba achieves state-of-the-art detection performance, while U-Mamba MTL further improves PCa detection through the auxiliary zonal segmentation task. These findings highlight the potential of integrating U-Mamba with anatomical context to improve PCa detection. The code and model weights are available at \url{https://github.com/mokkalokka/U-MambaMTL}.




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% Prostate cancer (PCa) remains a leading cause of cancer-related morbidity, necessitating accurate and non-invasive diagnostic tools. While deep learning models have advanced PCa detection in magnetic resonance imaging (MRI), they often lack integration of anatomical knowledge. This study evaluates U-Mamba, a deep learning architecture designed to enhance long-range dependency modeling with linear time complexity, for PCa detection. Additionally, a multi-task learning (MTL) extension, U-Mamba MTL, is introduced to incorporate prostate zonal anatomy, aligning with clinical diagnostic workflows. The models were evaluated using diverse datasets, including the PI-CAI hidden tuning cohort (N=100) and an in-house collected out-of-distribution cohort (N=200). Results demonstrate that U-Mamba achieves robust state-of-the-art detection performance, with U-Mamba MTL further improving tumor detection through the auxiliary zonal segmentation task. These findings underscore the potential of combining U-Mamba with anatomical context to enhance PCa detection.
\end{abstract}



% Prostate cancer (PCa) is among the most prevalent cancers worldwide, and its diagnosis heavily relies on prostate biopsies, which carry significant health risks. Magnetic resonance imaging (MRI)-based PCa screening using the Prostate Imaging Reporting \& Data System (PI-RADS) guidelines is currently being used to reduce unnecessary prostate biopsies. However, high variability between readers is observed as a consequence of the differing experience of radiologists. Previous work has shown promising results for prostate cancer detection in bi-parametric MRI (bpMRI), which may serve as a decision support tool for radiologists in prostate cancer screening. Current AI models however fails to incorporate long range dependencies and lack the integration of anatomical context which the PI-RADS guidelines relies on. This paper explores the efficacy of U-Mamba, a deep learning architecture that enhances long-range dependency modeling with linear time complexity, in PCa segmentation. Additionally, a novel multi-task learning (MTL) extension, U-Mamba MTL, is proposed to incorporate zonal anatomy into segmentation tasks. The model is evaluated on a combination of publicly available datasets, including PI-CAI and in-house datasets, totaling 300 cases. Results demonstrate that U-Mamba MTL achieves superior performance compared to state-of-the-art architectures, particularly in tumor localization. These findings underscore the potential of integrating anatomical knowledge and long-range dependency modeling for robust and clinically relevant PCa detection.