Author

H.-s. Min

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 2 Biophysics· 1 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Assay:imaging· 4 Research Focus:cell type annotation· 3 Approach:deep learning· 3 Organism:human· 3 Research Focus:clustering· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Y. Park· 4 shared d. ryu· 3 shared g. kim· 3 shared H. cho· 3 shared Y. Jo· 2 shared d. ahn· 1 shared D. Cho· 1 shared D. Lim· 1 shared

Activity by Year

2018-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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4 shared papersBioengineering

Y. Park

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Revealing 3D cancer tissue structures using holotomography and virtual hematoxylin and eosin staining via deep learning

J. park, S.-J. Shin, M. Kim, g. kim, H. cho, d. ryu, d. ahn, j. e. heo, h.-s. min, K. S. Lee, Y. Park, T. H. Hwang

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.

bioRxiv · Dec 4, 2023 · PreprintCancer Biology

Label-free bone marrow white blood cell classification using refractive index tomograms and deep learning

D. Ryu, J. Kim, D. Lim, H.-S. Min, I. You, D. Cho, Y. Park

This paper presents a label-free method for classifying bone marrow white blood cells using 3D refractive index tomograms and deep learning, achieving high accuracy in subtype classification.

bioRxiv · Nov 13, 2020Bioengineering

Data-driven multiplexed microtomography of endogenous subcellular dynamics

Y. Jo, H. Cho, W. S. Park, G. Kim, D. Ryu, Y. S. Kim, M. Lee, H. Joo, H. Jo, S. Lee, H.-s. Min, W. D. Heo, Y. Park

This paper introduces a data-driven method for multiplexed microtomography using refractive index imaging to study endogenous subcellular dynamics without exogenous labels.

bioRxiv · Sep 16, 2020Bioengineering

Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells

G. Kim, Y. Jo, H. Cho, H.-s. Min, Y. Park

This paper develops a label-free method using quantitative phase imaging and machine learning to screen for hematologic disorders with high accuracy.

bioRxiv · Jul 27, 2018Biophysics