75 citations · 78 across the 5 of their papers we have counts for
6 papers
RepConTUR: A Report-Conditioned Volumetric Multimodal Model for Abdominal Tumor Segmentation
Seunghwan Lee, Sangjoon Park, Jongmin Lee +4
Tumor delineation is required for radiotherapy planning, yet automatic segmentation is limited by errors, predominantly false positives, that distort dose and increase editing time…
Susceptibility of Large Language Models to User-Driven Factors in Medical Queries
Kyung Ho Lim, Ujin Kang, Xiang Li +4
Large language models (LLMs) are increasingly used in healthcare, but their reliability is heavily influenced by user-driven factors such as question phrasing and the completeness…
Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective
Yujin Oh, Pengfei Jin, Sangjoon Park +6
Ensuring fairness in medical image segmentation is critical due to biases in imbalanced clinical data acquisition caused by demographic attributes (e.g., age, sex, race) and clinic…
Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation
Yujin Oh, Sangjoon Park, Xiang Li +21
Clinical decision-making reflects diverse strategies shaped by regional patient populations and institutional protocols. However, most existing medical artificial intelligence (AI)…
End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding
Kwanyoung Kim, Yujin Oh, Sangjoon Park +5
Recent advances in AI foundation models have significant potential for lightening the clinical workload by mimicking the comprehensive and multi-faceted approaches used by medical…
LLM-driven Multimodal Target Volume Contouring in Radiation Oncology
Yujin Oh, Sangjoon Park, Hwa Kyung Byun +4
Target volume contouring for radiation therapy is considered significantly more challenging than the normal organ segmentation tasks as it necessitates the utilization of both imag…