14 papers
Surprise-Guided MergeSort: Budget-Efficient Human-in-the-Loop Ranking via Adaptive Comparison Scheduling
Yujin Park, Haejun Chung, Ikbeom Jang
Pairwise comparison is the gold standard for subjective ranking tasks; however, exhaustive annotation requires a massive number of human comparisons (). While sorting-based…
MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality
Yujin Park, Haejun Chung, Ikbeom Jang
Image quality in modern imaging systems emerges from the coupled effects of the sensor, optics, and computational reconstruction. Ultra-thin metalenses offer a path toward substant…
OTCHA: Optimal Transport-driven Confidence-aware Latent Hub Alignment for Multi-View Medical Image Classification
Jiwoong Yang, Haejun Chung, Ikbeom Jang
Multi-view imaging, such as mammography and chest radiography, is a standard component of clinical practice. However, medical images are often unregistered and contain view-specifi…
When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning
Daehwan Kim, Haejun Chung, Ikbeom Jang
Medical tabular data are ubiquitous in clinical research, but deep learning for tables remains underexplored because reliable labels often require costly expert adjudication, even…
CSWinUNETR: Segmentation of Thin Anatomical Structures in Medical Images
Junho Moon, Haejun Chung, Ikbeom Jang
Accurate segmentation of thin, tortuous anatomical structures, such as retinal vessels, cerebral vasculature, and facial wrinkles, remains challenging due to low contrast, frequent…
Hierarchical mutual distillation for multi-view fusion: Learning from all possible view combinations
Jiwoong Yang, Haejun Chung, Ikbeom Jang
Multi-view learning often struggles to effectively leverage images captured from diverse angles and locations. Learning methods for unstructured multi-view images remain largely un…