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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Article
  • Peer Reviewed
UC Riverside Previously Published Works (2022)
Frequently, neural network training involving biological images suffers from a lack of data, resulting in inefficient network learning. This issue stems from limitations in terms of time, resources, and difficulty in cellular experimentation and data collection. For example, when performing experimental analysis, it may be necessary for the researcher to use most of their data for testing, as opposed to model training. Therefore, the goal of this paper is to perform dataset augmentation using generative adversarial networks (GAN) to increase the classification accuracy of deep convolutional neural networks (CNN) trained on induced pluripotent stem cell microscopy images. The main challenges are: 1. modeling complex data using GAN and 2. training neural networks on augmented datasets that contain generated data. To address these challenges, a temporally constrained, hierarchical classification scheme that exploits domain knowledge is employed for model learning. First, image patches of cell colonies from gray-scale microscopy images are generated using GAN, and then these images are added to the real dataset and used to address class imbalances at multiple stages of training. Overall, a 2% increase in both true positive rate and F1-score is observed using this method as compared to a straightforward, imbalanced classification network, with some greater improvements on a classwise basis. This work demonstrates that synergistic model design involving domain knowledge is key for biological image analysis and improves model learning in high-throughput scenarios.
    Cover page: Generative Adversarial Networks for Morphological–Temporal Classification of Stem Cell Images
    • Thesis
    • Peer Reviewed
    UC Riverside Electronic Theses and Dissertations (2024)

    Stem cell biology has the potential to solve many problems related to disease modeling, and personalized regenerative medicine. Experimental research involving stem cells is often quantified via non-invasive microscopy imaging to observe morphological behavior and determine the underlying mechanisms of observed patterns. The image data collected from these experiments are very large and require extensive by-hand analysis by a skilled biologist to evaluate experimental outcomes. To this end, computer vision programs aimed at feature extraction and classification have become an indispensable tool to accelerate and standardize the analytical pipeline. Furthermore, deep learning has automated these programs to improve the accuracy and reliability of the results obtained from analysis.

    There remain limitations imposed on deep learning that are a consequence of the unique circumstances under which the data are collected. For example, class imbalances, lack of data, ground-truth annotation, contiguous class boundaries, and multi-label images, to name a few. The goal of this thesis is to overcome these limitations through the novel use of biological features which inform and guide model design and implementation. The major works comprising this dissertation involve deep neural network-based methods for stem cell microscopy image classification. Specifically, these methods address complex biological dataset problems including contrastive learning for improving discrimination of overlapping features, unsupervised generative adversarial networks for supplementation of limited datasets, and semi-supervised, pseudo-labeling to overcome costly manual annotations. Paramount to the success of these projects is the use of domain knowledge in the form of biological relationships between image classes, temporal and dynamic features, and microscopy scale-space, which are exploited to improve classification performance and are shown to be indispensable in designing efficient and effective deep learning models.

    These novel video-bioinformatics methods are implemented to draw inferences of experimental endpoints from a dataset of microscopy images concerned with determining the effects of nicotine on a Huntington’s disease iPSC model. Colony regions of interest are detected from raw, time-lapse microscopy images and sorted into four separate morphological classes that correspond to major cellular phenotypes. The proportions of these classes within the images over time provides insights into the growth and developmental changes that occur during these experiments. Deep learning is a powerful tool that automates the analytical pipeline and reveals key features of input images that can be used to model stem cell differentiation.

      Cover page: Classification of Stem Cell Microscopy Images Using Deep Learning
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