3 citations · 3 across the 13 of their papers we have counts for
16 papers
MovieSTAGE: Scene, Transition, and Global Encoding for Movie-fMRI ADHD Classification
Boseong Kim, Haejun Chung, Ikbeom Jang
Naturalistic movie-fMRI provides a shared, temporally structured probe of brain dynamics, yet predictive models commonly rely on whole-run functional connectivity (FC) or temporall…
CoHyFuse: Condition-wise Hypergraph Fusion with Global Connectome in Task-fMRI
Boseong Kim, Haejun Chung, Ikbeom Jang
Task-fMRI connectomes reveal state-dependent neural reconfigurations, yet conventional methods marginalize these signals by aggregating distinct conditions into static pairwise gra…
Let Confidence Change, Not the Prediction: Prediction-Preserving Repair for Post-hoc Calibration
Daehwan Kim, Haejun Chung, Ikbeom Jang
Post-hoc calibration corrects reported confidence, yet a multiclass calibrator can also change the associated top-1 prediction. Accuracy captures only the net effect of these chang…
VLM-Aware Meta-Optic Front-End Design for Frozen Vision-Language Models
Chanik Kang, Raphaël Pestourie, Haejun Chung
Conventional machine-vision pipelines typically rely on high-quality optics that produce clean, human-interpretable images, and optical design has therefore been driven by image-le…
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…
Dodgersort: Uncertainty-Aware VLM-Guided Human-in-the-Loop Pairwise Ranking
Yujin Park, Haejun Chung, Ikbeom Jang
Pairwise comparison labeling is emerging as it yields higher inter-rater reliability than conventional classification labeling, but exhaustive comparisons require quadratic cost. W…
EZ-Sort: Efficient Pairwise Comparison via Zero-Shot CLIP-Based Pre-Ordering and Human-in-the-Loop Sorting
Yujin Park, Haejun Chung, Ikbeom Jang
Pairwise comparison is often favored over absolute rating or ordinal classification in subjective or difficult annotation tasks due to its improved reliability. However, exhaustive…
BOLDSimNet: Examining Brain Network Similarity between Task and Resting-State fMRI
Boseong Kim, Debashis Das Chakladar, Haejun Chung +1
Traditional causal connectivity methods in task-based and resting-state functional magnetic resonance imaging (fMRI) face challenges in accurately capturing directed information fl…
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…
Ordinal-Aware Calibration for Ordinal Classification
Daehwan Kim, Haejun Chung, Ikbeom Jang
Deep neural networks frequently produce overconfident, miscalibrated predictions. In ordinal classification, predictions must also adhere to a unimodal and order-consistent structu…
Facial Wrinkle Segmentation for Cosmetic Dermatology: Pretraining with Texture Map-Based Weak Supervision
Junho Moon, Haejun Chung, Ikbeom Jang
Facial wrinkle detection plays a crucial role in cosmetic dermatology. Precise manual segmentation of facial wrinkles is challenging and time-consuming, with inherent subjectivity…
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Junho Moon, Symac Kim, Haejun Chung +1
Positron emission tomography (PET) provides molecular biomarkers for Alzheimer's disease and related dementias (ADRD) and is increasingly used for diagnosis, staging, and clinical…