Learning to segment prostate cancer by aggressiveness from scribbles in bi-parametric MRI. (arXiv:2207.05056v1 [eess.IV])

In this work, we propose a deep U-Net based model to tackle the challenging
task of prostate cancer segmentation by aggressiveness in MRI based on weak
scribble annotations. This model extends the size constraint loss proposed by
Kervadec et al. 1 in the context of multiclass detection and segmentation task.
This model is of high clinical interest as it allows training on prostate
biopsy samples and avoids time-consuming full annotation process. Performance
is assessed on a private dataset (219 patients) …

arxiv bi cancer learning parametric