Generation and Simulation of Yeast Microscopy Imagery with Deep Learning

• URL: http://arxiv.org/abs/2103.11834v3
• Date: Wed, 24 Mar 2021 13:28:37 GMT
• Title: Generation and Simulation of Yeast Microscopy Imagery with Deep Learning
• Authors: Christoph Reich
• Abstract summary: Time-lapse fluorescence microscopy (TLFM) is an important tool in synthetic biological research. This thesis is a study towards deep learning-based modeling of TLFM experiments on the image level.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Time-lapse fluorescence microscopy (TLFM) is an important and powerful tool in synthetic biological research. Modeling TLFM experiments based on real data may enable researchers to repeat certain experiments with minor effort. This thesis is a study towards deep learning-based modeling of TLFM experiments on the image level. The modeling of TLFM experiments, by way of the example of trapped yeast cells, is split into two tasks. The first task is to generate synthetic image data based on real image data. To approach this problem, a novel generative adversarial network, for conditionalized and unconditionalized image generation, is proposed. The second task is the simulation of brightfield microscopy images over multiple discrete time-steps. To tackle this simulation task an advanced future frame prediction model is introduced. The proposed models are trained and tested on a novel dataset that is presented in this thesis. The obtained results showed that the modeling of TLFM experiments, with deep learning, is a proper approach, but requires future research to effectively model real-world experiments.

Related papers

• A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data [55.748186000425996]
  Recent advancements show that diffusion models can generate high-quality images. We study this phenomenon in a hierarchical generative model of data. Our analysis characterises the relationship between time and scale in diffusion models.
  arXiv  Detail & Related papers  (2024-02-26T19:52:33Z)
• Discovering interpretable models of scientific image data with deep learning [0.0]
  We implement representation learning, sparse deep neural network training and symbolic regression. We demonstrate their relevance to the field of bioimaging using a well-studied test problem of classifying cell states in microscopy data. We explore the utility of such interpretable models in producing scientific explanations of the underlying biological phenomenon.
  arXiv  Detail & Related papers  (2024-02-05T15:45:55Z)
• Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images [6.251942628138834]
  Existing intensity-based localisation methods are not developed for imaging with a scanning microscope. Low signal-to-noise ratios, movement of molecules out-of-focus, and high motion blur on images recorded with scanning two-photon microscopy (2PM) in vivo pose a challenge to the accurate localisation of molecules. We developed a 2PM image simulator to supplement scarce training data.
  arXiv  Detail & Related papers  (2023-03-17T14:48:50Z)
• Online simulator-based experimental design for cognitive model selection [74.76661199843284]
  We propose BOSMOS: an approach to experimental design that can select between computational models without tractable likelihoods. In simulated experiments, we demonstrate that the proposed BOSMOS technique can accurately select models in up to 2 orders of magnitude less time than existing LFI alternatives.
  arXiv  Detail & Related papers  (2023-03-03T21:41:01Z)
• An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design [55.62660894625669]
  Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks. We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
  arXiv  Detail & Related papers  (2022-10-27T20:53:39Z)
• Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Data [74.66568380558172]
  We study the transferability of pre-trained models based on synthetic data generated by graphics simulators to downstream tasks. We introduce Task2Sim, a unified model mapping downstream task representations to optimal simulation parameters. It learns this mapping by training to find the set of best parameters on a set of "seen" tasks. Once trained, it can then be used to predict best simulation parameters for novel "unseen" tasks in one shot.
  arXiv  Detail & Related papers  (2021-11-30T19:25:27Z)
• Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy [23.720106678247888]
  We propose Multi-StyleGAN as a descriptive approach to simulate time-lapse fluorescence microscopy imagery of living cells. This novel generative adversarial network synthesises a multi-domain sequence of consecutive timesteps. The simulation captures underlying biophysical factors and time dependencies, such as cell morphology, growth, physical interactions, as well as the intensity of a fluorescent reporter protein.
  arXiv  Detail & Related papers  (2021-06-15T16:51:16Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data [0.0]
  Researchers rely heavily on computer simulations to explore the design space in search of high-performing implosions. For more effective design and investigation, simulations require input from past experimental data to better predict future performance. We describe a cognitive simulation method for combining simulation and experimental data into a common, predictive model.
  arXiv  Detail & Related papers  (2021-03-19T02:00:14Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Learning Numerical Observers using Unsupervised Domain Adaptation [13.548174682737756]
  Medical imaging systems are commonly assessed by use of objective image quality measures. Supervised deep learning methods have been investigated to implement numerical observers for task-based image quality assessment. labeling large amounts of experimental data to train deep neural networks is tedious, expensive, and prone to subjective errors.
  arXiv  Detail & Related papers  (2020-02-03T22:58:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.