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Journal of Imaging, Volume 11, Issue 4
April 2025 - 35 articles
Cover Story: Prostate cancer (PCa) is the second most common malignancy among men worldwide; however, it is highly curable if detected early. Hence, the main clinical challenge is to accurately identify those with and without cancer as early as possible. This paper introduces a novel multi-encoder cross-attention 3D architecture for assessing PCa presence in whole bi-parametric magnetic resonance imaging (MRI) volumes. With an architecture specifically designed to exploit complementary imaging features alongside clinical variables and the ProstateNET Imaging Archive, the largest image database worldwide for PCa mpMRI data, this study establishes new baselines for performances. The proposed method paves the way towards the clinical adoption of deep learning models for accurately determining the presence of PCa in patient populations. View this paper
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J. Imaging - ISSN 2313-433X