LNMBench shows existing noisy-label methods degrade sharply under high and realistic noise in medical images due to class imbalance and domain shifts, and proposes a simple robustness fix.
Medical image analysis 42, 60–88
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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.
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Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook
LNMBench shows existing noisy-label methods degrade sharply under high and realistic noise in medical images due to class imbalance and domain shifts, and proposes a simple robustness fix.
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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.