Despite the growing prominence of transformers in medical image segmentation, their application to clinically significant prostate cancer (csPCa) has been overlooked. Minimal attention has been paid to domain shift analysis and uncertainty assessment, critical for safely implementing computer-aided diagnosis (CAD) systems. Domain shift in medical imagery refers to differences between the data used to train a model and the data evaluated later, arising from variations in imaging equipment, protocols, patient populations, and acquisition noise. While recent models enhance in-domain performance, areas such as robustness and uncertainty estimation in out-of-domain distributions have received limited investigation, creating indecisiveness about model reliability. In contrast, our study addresses csPCa at voxel, lesion, and image levels, investigating models from traditional U-Net to cutting-edge transformers. We focus on four key points: robustness, calibration, out-of-distribution (OOD), and misclassification detection (MD). Findings show that transformer-based models exhibit enhanced robustness at image and lesion levels, both in and out of domain. However, this improvement is not fully translated to the voxel level, where Convolutional Neural Networks (CNNs) outperform in most robustness metrics. Regarding uncertainty, hybrid transformers and transformer encoders performed better, but this trend depends on misclassification or out-of-distribution tasks.
Keywords: Calibration; Misclassification detection; Out-of-distribution; Prostate cancer; Robustness; Vision transformers.
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