Fig. 2 | Scientific Reports

Fig. 2

From: Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector

Fig. 2

Visualization of the training and validation/inference protocol for the models described in this work. Training was performed using either T2-weighted or biparametric MRI studies belonging to either ProstateNet (PNet), PI-CAI or ProstateNet + PI-CAI (PNetCAI) to detect lesions annotated by radiologists. The validation/inference protocol consists in detecting lesions, extracting the most relevant lesion candidates37 and considering only lesions with an overlap of at least 10% with the whole prostate gland as inferred by a deep-learning model for prostate segmentation11. The patient aggressive lesion probability is then used in a recommendation system, while the binary/probabilistic prediction is used for visualization.

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