Hudanyun Sheng

Hudanyun Sheng

she/her (Dannie)

Data Scientist | Machine Learning | Computer Vision | AI

Publications

Increasing the Throughput of Annotation Tasks Across Scales of Plant Phenotyping Experiments

Published in NAPPN2024, 2023

This paper is about applying deep learning based approach for nuclei segmentation and tumor microenvironment characterization.

Recommended citation: Sheng H, Gutierrez J, Schuhl H, Murphy KM, Acosta-Gamboa L, Gehan M, Fahlgren N. Increasing the Throughput of Annotation Tasks Across Scales of Plant Phenotyping Experiments. Authorea Preprints. 2023 Oct 19. https://www.techrxiv.org/doi/full/10.22541/essoar.169773045.57471797

A Deep Learning Approach for Histology-Based Nuclei Segmentation and Tumor Microenvironment Characterization

Published in Pathology, 2023

This paper is about applying deep learning based approach for nuclei segmentation and tumor microenvironment characterization.

Recommended citation: Rong R, Sheng H, Jin KW, Wu F, Luo D, Wen Z, Tang C, Yang DM, Jia L, Amgad M, Cooper LA. A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization. Modern Pathology. 2023 Aug 1;36(8):100196. https://www.sciencedirect.com/science/article/pii/S0893395223001011

A Deep Learning Approach for Histology-Based Nuclei Segmentation and Tumor Microenvironment Characterization

Published in New Phytologist, 2023

This paper is about applying deep learning based approach for nuclei segmentation and tumor microenvironment characterization.

Recommended citation: Panda K, Mohanasundaram B, Gutierrez J, McLain L, Castillo SE, Sheng H, Casto A, Gratacós G, Chakrabarti A, Fahlgren N, Pandey S. The plant response to high CO2 levels is heritable and orchestrated by DNA methylation. New Phytologist. 2023 Jun;238(6):2427-39. https://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.18876

Root identification in minirhizotron imagery with multiple instance learning

Published in Machine Vision and Applications, 2020

This paper is about applying multiple instance learning for an image segmentation task (root segmentation) from minirhizotron images.

Recommended citation: "Yu, G., Zare, A., Sheng, H., Matamala, R., Reyes-Cabrera, J., Fritschi, F.B. and Juenger, T.E., 2020. Root identification in minirhizotron imagery with multiple instance learning. Machine Vision and Applications, 31, pp.1-13." /files/1903.03207.pdf