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cs.CV2026
A Comparative Study of Machine Learning and Deep Learning for Out-of-Distribution Detection
Jihyeon Baek, Seunghoon Lee, Gitaek Kwon +1
Out-of-distribution (OOD) detection is essential for building reliable AI systems, as models that produce outputs for invalid inputs cannot be trusted. Although deep learning (DL)…
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.CV2025
Towards the Automatic Segmentation, Modeling and Meshing of the Aortic Vessel Tree from Multicenter Acquisitions: An Overview of the SEG.A. 2023 Segmentation of the Aorta Challenge
Yuan Jin, Antonio Pepe, Gian Marco Melito +36
The automated analysis of the aortic vessel tree (AVT) from computed tomography angiography (CTA) holds immense clinical potential, but its development has been impeded by a lack o…