| A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei JM Phillip, KS Han, WC Chen, D Wirtz, PH Wu Nature protocols 16 (2), 754-774, 2021 | 84 | 2021 |
| CODA: quantitative 3D reconstruction of large tissues at cellular resolution AL Kiemen, AM Braxton, MP Grahn, KS Han, JM Babu, R Reichel, ... Nature Methods 19 (11), 1490-1499, 2022 | 68 | 2022 |
| Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP) S Jain, L Pei, JM Spraggins, M Angelo, JP Carson, N Gehlenborg, F Ginty, ... Nature cell biology 25 (8), 1089-1100, 2023 | 42 | 2023 |
| In situ characterization of the 3D microanatomy of the pancreas and pancreatic cancer at single cell resolution A Kiemen, AM Braxton, MP Grahn, KS Han, JM Babu, R Reichel, F Amoa, ... BioRxiv, 2020.12. 08.416909, 2020 | 39 | 2020 |
| High‐Resolution 3D Printing of Pancreatic Ductal Microanatomy Enabled by Serial Histology AL Kiemen, A Forjaz, R Sousa, KS Han, RH Hruban, LD Wood, PH Wu, ... Advanced Materials Technologies, 2301837, 2024 | 2 | 2024 |
| SenNet Case Selection Protocol| JHU TMC KS Han, F Wu, S Reddy, D Wirtz, J Sunshine, PH Wu | | 2024 |
| CODA (part 3): deep learning tissue structures labeling| HuBMAP| JHU-TMC KS Han, PH Wu, J Sunshine, A Kiemen, S Reddy, D Wirtz | | 2024 |
| CODA (part 4): register the deep learning labelled images and Construct 3D tissue matrix| HuBMAP| JHU-TMC KS Han, PH Wu, J Sunshine, A Kiemen, S Reddy, D Wirtz | | 2024 |
| Donor Case Selection| HuBMAP| JHU-TMC KS Han, J Sunshine, S Reddy, D Wirtz, PH Wu | | 2024 |
| Paraffin embedding of tissue specimen| HuBMAP| JHU-TMC KS Han, PH Wu, J Sunshine, A Kiemen, M James, S Reddy, D Wirtz | | 2024 |
| Tissue Fixation| HubMAP| JHU-TMC KS Han, PH Wu, J Sunshine, A Kiemen, M James, S Reddy, D Wirtz | | 2024 |
| Tissue Harvesting| HuBMAP| JHU-TMC KS Han, PH Wu, J Sunshine, A Kiemen, S Reddy, D Wirtz | | 2024 |
| Tissue H&E Staining| HuBMAP| JHU-TMC KS Han, PH Wu, J Sunshine, A Kiemen, M James, S Reddy, D Wirtz | | 2024 |
| CODA (part 6): register the nuclear coordinates and construct 3D cell matrix| HuBMAP| JHU-TMC KS Han, PH Wu, J Sunshine, A Kiemen, S Reddy, D Wirtz | | 2024 |
| CODA (part 1): setting up environment and preparing sample dataset| HuBMAP| JHU-TMC KS Han, PH Wu, J Sunshine, S Reddy, A Kiemen, D Wirtz | | 2024 |
| CODA: 3D tissue reconstruction pipeline| HuBMAP| JHU-TMC KS Han, PH Wu, J Sunshine, A Kiemen, S Reddy, D Wirtz | | 2024 |
| CODA (part 2): calculate registration on low-resolution tissue images| HuBMAP| JHU-TMC KS Han, PH Wu, J Sunshine, A Kiemen, S Reddy, D Wirtz Protocols.io, 2024 | | 2024 |
| CODA: shorthand for calling functions| HuBMAP| JHU-TMC KS Han, PH Wu, J Sunshine, A Kiemen, S Reddy, D Wirtz Protocols.io, 2024 | | 2024 |
| CODA (part 5): nuclear coordinate generation| HuBMAP| JHU-TMC KS Han, PH Wu, J Sunshine, A Kiemen, S Reddy, D Wirtz | | 2024 |
| Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping S Joshi, A Forjaz, K Sang Han, Y Shen, V Queiroga, D Xenes, J Matelsk, ... bioRxiv, 2024.03. 07.583909, 2024 | | 2024 |
Pei-Hsun WuJohns Hopkins UniversityVerified email at jhu.edu
