Neural network fast-classifies biological images using features selected after their random-forests-importance to power smart microscopy

TL;DR

This paper develops a real-time, efficient cell classification method for smart microscopy by selecting high-impact features and employing neural networks, achieving high accuracy with minimal computational delay.

Contribution

It introduces a feature selection strategy based on random forests importance to optimize neural network classification for smart microscopy.

Findings

Random forests outperform Fisher's linear discriminant in classification.

Selected features can be reduced without significant accuracy loss.

Achieved 79.6% accuracy at 14 cells/sec on embedded systems.

Abstract

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, to make acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced dataset due to cost and time to prepare the samples and have the datasets annotated by experts. We propose here 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 for the sake of understanding 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 any significant loss in accuracy, 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 $\pm$ 2.4 % accurate classification of a cell took 68.7 $\pm$ 3.5 ms (mean $\pm$ SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into 8 phases of the cell cycle using 12 feature-groups and operating a consumer market ARM-based embedded system. Interestingly, 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 optimising these algorithms for smart microscopy.