Keywords: Multimodal, Cancer
Prostate Imaging Reporting and Data System (PI-RADS) on multiparametric MRI (mpMRI) provides fundamental MRI interpretation guidelines but suffers from inter-reader variability. Deep learning networks show great promise in automatic lesion segmentation and classification, which help to ease the burden on radiologists and reduce inter-reader variability. In this study, we proposed a novel multi-branch network, MiniSegCaps, for prostate cancer segmentation and PI-RADS classification on mpMRI, and a graphical user interface (GUI) integrated into the clinical workflow for diagnosis reports generation. Our model achieved the best performance in prostate cancer segmentation and PIRADS classification compared with state-of-the-art methods.
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