CORTEXA
← Browse

Kwang-Hyun Uhm

3 papers indexed

arxivcs.CV2026-07-14

MAGE: Color-Invariant and Spatial Knowledge Distillation for Gastric Neoplasm Classification

Jiho Jun, Jeongwon Woo, Jaemin Song, Thanh Bong Nguyen, Dong-heon Yeon, Donghoon Kang, et al.

Accurate differentiation between gastric adenoma and carcinoma during endoscopy is critical for clinical decision-making. Yet, this task is highly challenging due to high inter-class similarity and ambiguous boundaries between the two classes. Existing ROI-based classification me…

View free PDFSource page
arxivcs.CV2026-07-14

Decouple and Reason: Anatomically Guided Two-Stage Voxel-Level Grounding of Free-Text Findings in 3D Chest CT

Kwang-Hyun Uhm, Inhwa Son, Sung-Jea Ko

Automatic voxel-level grounding of free-text findings in 3D chest Computed Tomography (CT) is critical for clinical interpretability. However, this task remains highly challenging due to the intricate spatial complexity of large 3D volumes and the heterogeneity of free-text findi…

View free PDFSource page
arxivcs.CV2026-07-14

Lesion Segmentation in Moderate to Severe Traumatic Brain Injury: An nnU-Net Based Approach with Adaptive Normalization in the AIMS-TBI 2025 Challenge

Inhwa Son, Gaeun Lee, Sohyeon Sim, Kwang-Hyun Uhm

The segmentation of lesions in Moderate to Severe Traumatic Brain Injury (msTBI) from T1-weighted MRI presents a significant clinical challenge due to the profound heterogeneity of lesion characteristics in terms of size, shape, and location. To address this, the AIMS-TBI 2025 Ch…

View free PDFSource page