A review of generative adversarial networks (GANs) and its applications in a wide variety of disciplines: from medical to remote sensing
We look into Generative Adversarial Network (GAN), its prevalent variants and applications
in a number of sectors. GANs combine two neural networks that compete against one …
in a number of sectors. GANs combine two neural networks that compete against one …
Enhancing scientific discoveries in molecular biology with deep generative models
Generative models provide a well‐established statistical framework for evaluating
uncertainty and deriving conclusions from large data sets especially in the presence of …
uncertainty and deriving conclusions from large data sets especially in the presence of …
Three-dimensional Wadell roundness for particle angularity characterization of granular soils
Abstract The geologist Hakon Wadell proposed the roundness definition in the 1930s for
quantifying the particle angularity of granular soils. Due to the difficulty in obtaining three …
quantifying the particle angularity of granular soils. Due to the difficulty in obtaining three …
[HTML][HTML] Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation
J Cao, G Guan, VWS Ho, MK Wong, LY Chan… - Nature …, 2020 - nature.com
The invariant development and transparent body of the nematode Caenorhabditis elegans
enables complete delineation of cell lineages throughout development. Despite extensive …
enables complete delineation of cell lineages throughout development. Despite extensive …
[HTML][HTML] Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy
AL Jenner, M Smalley, D Goldman, WF Goins… - Iscience, 2022 - cell.com
Oncolytic viruses (OVs) are emerging cancer immunotherapy. Despite notable successes in
the treatment of some tumors, OV therapy for central nervous system cancers has failed to …
the treatment of some tumors, OV therapy for central nervous system cancers has failed to …
Dual projection generative adversarial networks for conditional image generation
Abstract Conditional Generative Adversarial Networks (cGANs) extend the standard
unconditional GAN framework to learning joint data-label distributions from samples, and …
unconditional GAN framework to learning joint data-label distributions from samples, and …
Lesion-aware contrastive representation learning for histopathology whole slide images analysis
Image representation learning has been a key challenge to promote the performance of the
histopathological whole slide images analysis. The previous representation learning …
histopathological whole slide images analysis. The previous representation learning …
[HTML][HTML] Unpaired mesh-to-image translation for 3D fluorescent microscopy images of neurons
Abstract While Generative Adversarial Networks (GANs) can now reliably produce realistic
images in a multitude of imaging domains, they are ill-equipped to model thin, stochastic …
images in a multitude of imaging domains, they are ill-equipped to model thin, stochastic …
Improved automatic detection of herpesvirus secondary envelopment stages in electron microscopy by augmenting training data with synthetic labelled images …
K Shaga Devan, P Walther, J von Einem… - Cellular …, 2021 - Wiley Online Library
Detailed analysis of secondary envelopment of the herpesvirus human cytomegalovirus
(HCMV) by transmission electron microscopy (TEM) is crucial for understanding the …
(HCMV) by transmission electron microscopy (TEM) is crucial for understanding the …
Robust conditional GAN from uncertainty-aware pairwise comparisons
Conditional generative adversarial networks have shown exceptional generation
performance over the past few years. However, they require large numbers of annotations …
performance over the past few years. However, they require large numbers of annotations …