Prostate Cancer Detection in Bi-Parametric MRI using Zonal Anatomy-Guided U-Mamba with Multi-Task Learning

Michael S. Larsen, Syed Farhan Abbas, Gabriel Kiss, Mattijs Elschot, Tone F. Bathen, Frank Lindseth
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:864-881, 2026.

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 https://github.com/mokkalokka/U-MambaMTL.

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-larsen26a, title = {Prostate Cancer Detection in Bi-Parametric MRI using Zonal Anatomy-Guided U-Mamba with Multi-Task Learning}, author = {Larsen, Michael S. and Abbas, Syed Farhan and Kiss, Gabriel and Elschot, Mattijs and Bathen, Tone F. and Lindseth, Frank}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {864--881}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/larsen26a/larsen26a.pdf}, url = {https://proceedings.mlr.press/v301/larsen26a.html}, 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 https://github.com/mokkalokka/U-MambaMTL.} }
Endnote
%0 Conference Paper %T Prostate Cancer Detection in Bi-Parametric MRI using Zonal Anatomy-Guided U-Mamba with Multi-Task Learning %A Michael S. Larsen %A Syed Farhan Abbas %A Gabriel Kiss %A Mattijs Elschot %A Tone F. Bathen %A Frank Lindseth %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-larsen26a %I PMLR %P 864--881 %U https://proceedings.mlr.press/v301/larsen26a.html %V 301 %X 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 https://github.com/mokkalokka/U-MambaMTL.
APA
Larsen, M.S., Abbas, S.F., Kiss, G., Elschot, M., Bathen, T.F. & Lindseth, F.. (2026). Prostate Cancer Detection in Bi-Parametric MRI using Zonal Anatomy-Guided U-Mamba with Multi-Task Learning. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:864-881 Available from https://proceedings.mlr.press/v301/larsen26a.html.

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