2 papers
cs.CV2024
Learning with Instance-Dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction
Po-Hsuan Huang, Chia-Ching Lin, Chih-Fan Hsu +2
Learning from noisy-labeled data is crucial for real-world applications. Traditional Noisy-Label Learning (NLL) methods categorize training data into clean and noisy sets based on…
cs.CV2024
Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentation
Shang-Jui Kuo, Po-Han Huang, Chia-Ching Lin +2
Diabetic foot ulcers pose health risks, including higher morbidity, mortality, and amputation rates. Monitoring wound areas is crucial for proper care, but manual segmentation is s…