Yapay Zeka · 3 dk okuma · 17 Nisan 2026
Transformer models outperform CNNs in prostate MRI segmentation
SwinUNETR achieves 5-point Dice improvement over standard UNet when trained on mixed-reader datasets, suggesting transformer attention handles annotation variability better.
Transformer-based models, particularly SwinUNETR, outperform convolutional networks on prostate gland segmentation across multiple readers and training strategies.
- — SwinUNETR achieved Dice scores 0.86–0.90 versus baseline UNet 0.83–0.85 on single-reader tasks.
- — Shifted-window self-attention reduces sensitivity to label noise and class imbalance from multiple annotators.
- — Mixed-cohort cross-validation with gland size stratification produced strongest results (0.90+ Dice).
- — UNETR performed worse than SwinUNETR on larger gland subsets, indicating architecture matters for anatomical variation.
- — Hyperparameter tuning via Optuna ensured fair comparison across all three model types.
- — Test set from independent readers validates generalization beyond training population.
- — Computational efficiency maintained despite transformer complexity gains.
Sık sorulanlar
- SwinUNETR combines global self-attention with shifted-window local attention, which better handles label noise and class imbalance from multiple annotators. UNETR uses only global attention, which may overfit to individual reader biases. The hybrid approach generalizes better across heterogeneous data.