Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues.
Mechanical cues such as stresses and strains are now recognized as essential regulators in many biological processes like cell division, gene expression or morphogenesis. Studying the interplay between these mechanical cues and biological responses requires experimental tools to measure these cues....
Main Authors: | Louis Combe, Mélina Durande, Hélène Delanoë-Ayari, Olivier Cochet-Escartin |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2023-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0281931 |
Similar Items
-
Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues
by: Louis Combe, et al.
Published: (2023-01-01) -
Stress-shape misalignment in confluent cell layers
by: Nejad, MR, et al.
Published: (2024) -
Interplay of curvature and rigidity in shape-based models of confluent tissue
by: Daniel M. Sussman
Published: (2020-06-01) -
On the origin of universal cell shape variability in confluent epithelial monolayers
by: Souvik Sadhukhan, et al.
Published: (2022-12-01) -
Extracardiac Fontan with T-shape conduit in non-confluent pulmonary arteries
by: Bae Eun, et al.
Published: (2008-02-01)