2 citations · 2 across the 4 of their papers we have counts for
4 papers · 1 filter
SelectiveKD: A semi-supervised framework for cancer detection in DBT through Knowledge Distillation and Pseudo-labeling
Laurent Dillard, Hyeonsoo Lee, Weonsuk Lee +3
When developing Computer Aided Detection (CAD) systems for Digital Breast Tomosynthesis (DBT), the complexity arising from the volumetric nature of the modality poses significant t…
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…
Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images
Hyeonsoo Lee, Junha Kim, Eunkyung Park +3
Recently, deep learning models have shown the potential to predict breast cancer risk and enable targeted screening strategies, but current models do not consider the change in the…
Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency
Hyeonsoo Lee, Won-Ki Jeong
Segmentation is a fundamental process in microscopic cell image analysis. With the advent of recent advances in deep learning, more accurate and high-throughput cell segmentation h…