6 citations · 10 across the 4 of their papers we have counts for
4 papers · 1 filter
SCORPION: Addressing Scanner-Induced Variability in Histopathology
Jeongun Ryu, Heon Song, Seungeun Lee +4
Ensuring reliable model performance across diverse domains is a critical challenge in computational pathology. A particular source of variability in Whole-Slide Images is introduce…
Bayesian Optimization Meets Self-Distillation
HyunJae Lee, Heon Song, Hyeonsoo Lee +3
Bayesian optimization (BO) has contributed greatly to improving model performance by suggesting promising hyperparameter configurations iteratively based on observations from multi…
Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
Mingu Kang, Heon Song, Seonwook Park +2
Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning has sh…
Variability Matters : Evaluating inter-rater variability in histopathology for robust cell detection
Cholmin Kang, Chunggi Lee, Heon Song +2
Large annotated datasets have been a key component in the success of deep learning. However, annotating medical images is challenging as it requires expertise and a large budget. I…