4 papers
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)…
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…
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…
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…