1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2026
Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation
Inhyuk Park, Doohyun Park
Noisy labels are common in large-scale medical imaging datasets due to inter-observer variability and ambiguous cases. We propose a statistically grounded and task-agnostic framewo…
cs.HC2026
When Prompts Mislead: Textual Dominance and Diagnostic Bias in MLLMs
Inhyuk Park, Doohyun Park
Multimodal large language models (MLLMs) are increasingly being evaluated for medical applications, where computational constraints often make prompting strategies the only practic…
cs.CV2023★ 1 cited
Robust Asymmetric Loss for Multi-Label Long-Tailed Learning
Wongi Park, Inhyuk Park, Sungeun Kim +1
In real medical data, training samples typically show long-tailed distributions with multiple labels. Class distribution of the medical data has a long-tailed shape, in which the i…