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Found 4 papers, 0 papers with code
ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans
In this work, we propose a novel end-to-end multi-class network that jointly segments the prostate gland and cancer lesions with GS group grading.
Perfusion imaging in deep prostate cancer detection from mp-MRI: can we take advantage of it?
To our knowledge, all deep computer-aided detection and diagnosis (CAD) systems for prostate cancer (PCa) detection consider bi-parametric magnetic resonance imaging (bp-MRI) only, including T2w and ADC sequences while excluding the 4D perfusion sequence, which is however part of standard clinical protocols for this diagnostic task.
Learning to segment prostate cancer by aggressiveness from scribbles in bi-parametric MRI
Performance is assessed on a private dataset (219 patients) where the full ground truth is available as well as on the ProstateX-2 challenge database, where only biopsy results at different localisations serve as reference.
Prostate Cancer Semantic Segmentation by Gleason Score Group in bi-parametric MRI with Self Attention Model on the Peripheral Zone
Performance of our model was compared to a U-Net baseline model to assess the impact of the self attention module on PCa detection.
