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.
Disc: Learning from noisy labels via dynamic instance-specific selection and correction, in:ProceedingsoftheIEEE/CVFconferenceoncomputervisionand pattern recognition, pp
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
ACCEPT 1representative citing papers
citing papers explorer
-
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.