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These findings are supported by the ROC curves of the models, showing the standard UNet having a generally lower true positive rate than the Attention and Nested UNets for any given false positive rate. It can also be seen that although the Attention UNet can get better true positive performance than the Nested UNet for generous thresholds, the Nested UNet marginally outperforms Attention UNet when minimizing false positives is prioritized.

For future research, it would be interesting to see how much performance can be increased with greater data. Another interesting experiment would be to see how these, and similar architectures perform on other types of scans (MRA) and what effect further synthetic data augmentation and synthetic data generation can have on performance.

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