Hierarchical transformers like SwinUNETR show better robustness to perturbations in a two-stage fluence map prediction pipeline for IMRT, with smooth degradation under moderate changes but sharp failures under severe ones, and SSIM fails to capture clinically relevant errors.
Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects
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A multi-dataset cross-domain knowledge distillation approach improves unified performance on medical image segmentation, classification, and detection by transferring domain-invariant features from a joint teacher model to task-specific students.
SAM (ViT-B) shows stable spleen segmentation in abdominal CT with mean Dice drop below 0.01 and no rise in failures under simulated domain shifts like noise, blur, and contrast changes.
citing papers explorer
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Robustness of Transformer-Based Fluence Map Prediction Under Clinically Realistic Perturbations
Hierarchical transformers like SwinUNETR show better robustness to perturbations in a two-stage fluence map prediction pipeline for IMRT, with smooth degradation under moderate changes but sharp failures under severe ones, and SSIM fails to capture clinically relevant errors.
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Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection
A multi-dataset cross-domain knowledge distillation approach improves unified performance on medical image segmentation, classification, and detection by transferring domain-invariant features from a joint teacher model to task-specific students.
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Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment
SAM (ViT-B) shows stable spleen segmentation in abdominal CT with mean Dice drop below 0.01 and no rise in failures under simulated domain shifts like noise, blur, and contrast changes.