Geon Kim

DeepHOPE uses deep learning and 3D imaging to assess pluripotency in hPSCs, detecting structural changes before molecular loss of pluripotency.

This paper develops a label-free deep learning framework using holotomography to classify cell death pathways with high accuracy.

This paper develops a 3D virtual H&E staining method using holotomography and deep learning for cancer tissues, validated on colon and gastric cancer samples.

This paper introduces a label-free 3D imaging technique to predict blastocyst formation in mouse embryos using machine learning.

This study introduces low-coherence holotomography for long-term live imaging of unlabeled mouse small intestinal organoids, capturing high-resolution morphological details and differentiating viable from non-viable organoids.

This study uses holotomography and machine learning to classify functional immune states of human monocytes, achieving high accuracy in identifying hyper-inflammatory and immune-suppressed states.

This paper introduces a deep learning framework for 3D label-free tracking of immunological synapses using optical diffraction tomography and segmentation.

This study presents 3D refractive index imaging of angiosperm pollen grains using optical diffraction tomography, enabling quantitative analysis of structural features without labeling.

This paper uses optical diffraction tomography to create 3D refractive index maps of Pinus pollen grains, revealing morphological features and interspecific differences.