Neural network fast‐classifies biological images through features selecting to power automated microscopy - Université de Rennes Accéder directement au contenu
Article Dans Une Revue Journal of Microscopy Année : 2022

Neural network fast‐classifies biological images through features selecting to power automated microscopy

Résumé

Artificial intelligence is nowadays used for cell detection and classification in optical microscopy during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart by making acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced data set due to cost and time to prepare the samples and have the data sets annotated by experts. We propose a real-time image processing compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning to understand the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher's linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without significant accuracy loss, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 ± 2.4% accurate classification of a cell took 68.7 ± 3.5 ms (mean ± SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into eight phases of the cell cycle, using 12 feature groups and operating a consumer market ARM-based embedded system. A simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimizing these algorithms for smart microscopy.
Fichier principal
Vignette du fichier
embedded-image-processing.pdf (3.56 Mo) Télécharger le fichier
Suppl_Fig_v18oct21_E+.pdf (554.43 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03408803 , version 1 (29-10-2021)

Identifiants

Citer

Maël Balluet, Florian Sizaire, Youssef El Habouz, Thomas Walter, Jérémy Pont, et al.. Neural network fast‐classifies biological images through features selecting to power automated microscopy. Journal of Microscopy, 2022, 285 (1), pp.3-19. ⟨10.1111/jmi.13062⟩. ⟨hal-03408803⟩
98 Consultations
130 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More