Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging

Ulicna, Kristina; Ho, Laure T L; Soelistyo, Christopher J; Day, Nathan J; Lowe, Alan R.

doi:10.1007/978-1-0716-2221-6_3

Cited by 3 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The cells were segmented (Weigert et al, 2020), classified (Ulicna et al, 2022) and tracked (Ulicna et al, 2021) to reconstruct multi-generational lineage trees (Fig. 1).…”

Section: Live-cell Imaging and Single-cell Trajectory Datasetmentioning

confidence: 99%

Learning dynamic image representations for self-supervised cell cycle annotation

Ulicna

Kelkar

Soelistyo

et al. 2023

Preprint

View full text Add to dashboard Cite

Time-based comparisons of single-cell trajectories are challenging due to their intrinsic heterogeneity, autonomous decisions, dynamic transitions and unequal lengths. In this paper, we present a self-supervised framework combining an image autoencoder with dynamic time series analysis of latent feature space to represent, compare and annotate cell cycle phases across single-cell trajectories. In our fully data-driven approach, we map similarities between heterogeneous cell tracks and generate statistical representations of single-cell trajectory phase durations, onset and transitions. This work is a first effort to transform a sequence of learned image representations from cell cycle-specific reporters into an unsupervised sequence annotation.

show abstract

“…The cells were segmented (Weigert et al, 2020), classified (Ulicna et al, 2022) and tracked (Ulicna et al, 2021) to reconstruct multi-generational lineage trees (Fig. 1).…”

Section: Live-cell Imaging and Single-cell Trajectory Datasetmentioning

confidence: 99%

Learning dynamic image representations for self-supervised cell cycle annotation

Ulicna

Kelkar

Soelistyo

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…As a result, this field has seen a recent explosion of studies applying deep learning [5]. The capability of DNNs to extract patterns from complex data has led to their application by cell biologists in tasks as varied as feature extraction [6,7,8,9], morphology-based classification [10,11,12,13,14,15,16], image segmentation [17,18,19,20,21,22,23,24] , synthetic data generation [25,26] and more.…”

Section: As a Tool In Bioimagingmentioning

confidence: 99%

Virtual perturbations to assess explainability of deep-learning based cell fate predictors

Soelistyo

Charras

Lowe

2023

Preprint

View full text Add to dashboard Cite

Explainable deep learning holds significant promise in extracting scientific insights from experimental observations. This is especially so in the field of bio-imaging, where the raw data is often voluminous, yet extremely variable and difficult to study. However, one persistent challenge in deep learning assisted scientific discovery is that the workings of artificial neural networks are often difficult to interpret. Here we present a simple technique for investigating the behaviour of trained neural networks: virtual perturbation. By making precise and systematic alterations to input data or internal representations thereof, we are able to discover causal relationships in the outputs of a deep learning model, and by extension, in the underlying phenomenon itself. As an exemplar, we use our recently described deep-learning based cell fate prediction model. We trained the network to predict the fate of less fit cells in an experimental model of mechanical cell competition. By applying virtual perturbation to the trained network, we discover causal relationships between a cell's environment and eventual fate. We compare these with known properties of the biological system under investigation to demonstrate that the model faithfully captures insights previously established by experimental research.

show abstract

“…As a result, this field has seen a recent explosion of studies applying deep learning [5]. The capability of DNNs to extract patterns from complex data has led to their application by cell biologists in tasks as varied as feature extraction [13,23,29,31], morphology-based classification [11,24,33,14,37,18,39], image segmentation [25,6,30,10,34,1,27,4] , synthetic data generation [21,8] and more.…”

Section: Deep Learning As a Tool In Bioimagingmentioning

confidence: 99%

Virtual perturbations to assess explainability of deep-learning based cell fate predictors

Soelistyo,

Charras,

Lowe

2023

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Self Cite

View full text Add to dashboard Cite

Explainable deep learning holds significant promise in extracting scientific insights from experimental observations. This is especially so in the field of bio-imaging, where the raw data is often voluminous, yet extremely variable and difficult to study. However, one persistent challenge in deep learning assisted scientific discovery is that the workings of artificial neural networks are often difficult to interpret. Here we present a simple technique for investigating the behavior of trained neural networks: virtual perturbation. By making precise and systematic alterations to input data or internal representations thereof, we are able to discover causal relationships in the outputs of a deep learning model, and by extension, in the underlying phenomenon itself. As an exemplar, we use a recently described deeplearning based cell fate prediction model. We trained the network to predict the fate of less fit cells in an experimental model of mechanical cell competition. By applying virtual perturbation to the trained network, we discover causal relationships between a cell's environment and eventual fate. We compare these with known properties of the biological system under investigation to demonstrate that the model faithfully captures insights previously established by experimental research.

show abstract

Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging

Cited by 3 publications

References 19 publications

Learning dynamic image representations for self-supervised cell cycle annotation

Learning dynamic image representations for self-supervised cell cycle annotation

Virtual perturbations to assess explainability of deep-learning based cell fate predictors

Virtual perturbations to assess explainability of deep-learning based cell fate predictors

Contact Info

Product

Resources

About