Author

I. Stavness

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

Biology Disciplines

Click to filter author papers

Plant Biology· 3 Bioengineering· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:imaging· 4 Research Focus:cell type annotation· 3 Approach:deep learning· 3 Organism:plant (other)· 3 Research Focus:clustering· 2 Research Focus:differential expression· 2 Approach:contrastive learning· 1 Approach:experimental assay· 1

Frequent Co-Authors

A. Ghanbari· 1 shared A. Pages· 1 shared B. Sagl· 1 shared C. Miao· 1 shared E. Piehslinger· 1 shared E. Rodene· 1 shared F. Maleki· 1 shared G. H. Shirdel· 1 shared

Activity by Year

2019-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 9 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperPlant Biology

A. Ghanbari

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

The effect of bolus properties on muscle activation patterns and TMJ loading during unilateral chewing

B. Sagl, M. Schmid-Schwap, E. Piehslinger, H. Yao, X. Rausch-Fan, I. Stavness

This study uses an in silico model to analyze how bolus properties affect muscle activation and TMJ loading during chewing, supporting clinical guidelines for reducing TMJ stress.

bioRxiv · Mar 2, 2023Bioengineering

Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns

K. Najafian, A. Ghanbari, M. S. Kish, M. Eramian, G. H. Shirdel, I. Stavness, L. Jin, F. Maleki

The paper proposes a semi-self-supervised learning method for wheat head segmentation using minimal manual annotations and achieves high Dice scores on both internal and external datasets.

bioRxiv · Aug 9, 2022Plant Biology

Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

J. R. Ubbens, M. Cieslak, P. Prusinkiewicz, I. Parkin, J. Ebersbach, I. Stavness

This paper introduces Latent Space Phenotyping (LSP), a method that automatically detects and quantifies treatment responses from images, demonstrating its effectiveness on synthetic datasets and an interspecific cross of Setaria.

bioRxiv · Oct 28, 2019Plant Biology

Simulated Plant Images Improve Maize Leaf Counting Accuracy

C. Miao, T. P. Hoban, A. Pages, Z. Xu, E. Rodene, J. Ubbens, I. Stavness, J. Yang, J. C. Schnable

The paper shows synthetic maize leaf images improve leaf counting accuracy when real-world data are limited, outperforming real data in some cases.

bioRxiv · Jul 18, 2019 · PreprintPlant Biology