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Insights into Imaging
Fig. 4 | Insights into Imaging

Fig. 4

From: Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study

Fig. 4

The AUROC, FROC, and PR curves of the nnU-Net in detecting clinically significant prostate cancer with and without transfer learning. The area under the receiver operating characteristic (AUROC), Free-Response Receiver Operating Characteristic (FROC), and Precision–Recall (PR) curves of the ensemble of five nnU-Net models in detecting clinically significant prostate cancer in the in-house dataset with and without transfer learning. The AUROC and FROC slightly decreased, and average precision slightly increased using transfer learning, not reaching a statistical significance

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