Generative adversarial networks in cell microscopy for image augmentation. A systematic review
Cell microscopy is the main tool that allows researchers to study microorganisms and plays
a key role in observing and understanding the morphology, interactions, and development of …
a key role in observing and understanding the morphology, interactions, and development of …
[HTML][HTML] Enhanced cell segmentation with limited annotated data using generative adversarial networks
The application of deep learning is rapidly transforming the field of bioimage analysis. While
deep learning has shown great promise in complex microscopy tasks such as single-cell …
deep learning has shown great promise in complex microscopy tasks such as single-cell …
Microscopic image augmentation using an enhanced WGAN
H Xu, C Li, J Zhang, Z Li, C Sun, X Zhao - The fourth international …, 2020 - dl.acm.org
The main obstacle to image augmentation with Generative Adversarial Networks (GANs) is
the need for a large amount of training data, but this is difficult for small datasets like …
the need for a large amount of training data, but this is difficult for small datasets like …
[HTML][HTML] Generative adversarial networks for augmenting training data of microscopic cell images
P Baniukiewicz, EJ Lutton, S Collier… - Frontiers in Computer …, 2019 - frontiersin.org
Generative adversarial networks (GANs) have recently been successfully used to create
realistic synthetic microscopy cell images in 2D and predict intermediate cell stages. In the …
realistic synthetic microscopy cell images in 2D and predict intermediate cell stages. In the …
Pathology-aware generative adversarial networks for medical image augmentation
C Han - arXiv preprint arXiv:2106.01915, 2021 - arxiv.org
Convolutional Neural Networks (CNNs) can play a key role in Medical Image Analysis under
large-scale annotated datasets. However, preparing such massive dataset is demanding. In …
large-scale annotated datasets. However, preparing such massive dataset is demanding. In …
An enhanced framework of generative adversarial networks (EF-GANs) for environmental microorganism image augmentation with limited rotation-invariant training …
The main obstacle to image augmentation with Generative Adversarial Networks (GANs) is
the need for a large amount of training data, but this is difficult for small datasets like …
the need for a large amount of training data, but this is difficult for small datasets like …
Conditioned generative transformers for histopathology image synthetic augmentation
Deep learning networks have demonstrated state-of-the-art performance on medical image
analysis tasks. However, the majority of the works rely heavily on abundantly labeled data …
analysis tasks. However, the majority of the works rely heavily on abundantly labeled data …
Synthetic Generation of 3D Microscopy Images using Generative Adversarial Networks
H Narotamo, M Ouarné, CA Franco… - 2022 44th Annual …, 2022 - ieeexplore.ieee.org
Fluorescence microscopy images of cell organelles enable the study of various complex
biological processes. Recently, deep learning (DL) models are being used for the accurate …
biological processes. Recently, deep learning (DL) models are being used for the accurate …
Quality assessment of synthetic fluorescence microscopy images for image segmentation
Synthetic images are widely used in image segmentation for algorithm training and
performance assessment. Recently, advances in image synthesis techniques, especially …
performance assessment. Recently, advances in image synthesis techniques, especially …
Using conditional Generative Adversarial Networks (GAN) to generate de novo synthetic cell nuclei for training machine learning-based image segmentation
MI Cosacak, C Kizil - BioRxiv, 2022 - biorxiv.org
Generating masks on training data for augmenting machine learning is one of the
challenges as it is time-consuming when performed manually. While variable random …
challenges as it is time-consuming when performed manually. While variable random …