15 research outputs found
Multi-Adversarial Variational Autoencoder Networks
The unsupervised training of GANs and VAEs has enabled them to generate
realistic images mimicking real-world distributions and perform image-based
unsupervised clustering or semi-supervised classification. Combining the power
of these two generative models, we introduce Multi-Adversarial Variational
autoEncoder Networks (MAVENs), a novel network architecture that incorporates
an ensemble of discriminators in a VAE-GAN network, with simultaneous
adversarial learning and variational inference. We apply MAVENs to the
generation of synthetic images and propose a new distribution measure to
quantify the quality of the generated images. Our experimental results using
datasets from the computer vision and medical imaging domains---Street View
House Numbers, CIFAR-10, and Chest X-Ray datasets---demonstrate competitive
performance against state-of-the-art semi-supervised models both in image
generation and classification tasks
Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks
A Magnetic Resonance Imaging (MRI) exam typically consists of the acquisition
of multiple MR pulse sequences, which are required for a reliable diagnosis.
Each sequence can be parameterized through multiple acquisition parameters
affecting MR image contrast, signal-to-noise ratio, resolution, or scan time.
With the rise of generative deep learning models, approaches for the synthesis
of MR images are developed to either synthesize additional MR contrasts,
generate synthetic data, or augment existing data for AI training. However,
current generative approaches for the synthesis of MR images are only trained
on images with a specific set of acquisition parameter values, limiting the
clinical value of these methods as various sets of acquisition parameter
settings are used in clinical practice. Therefore, we trained a generative
adversarial network (GAN) to generate synthetic MR knee images conditioned on
various acquisition parameters (repetition time, echo time, image orientation).
This approach enables us to synthesize MR images with adjustable image
contrast. In a visual Turing test, two experts mislabeled 40.5% of real and
synthetic MR images, demonstrating that the image quality of the generated
synthetic and real MR images is comparable. This work can support radiologists
and technologists during the parameterization of MR sequences by previewing the
yielded MR contrast, can serve as a valuable tool for radiology training, and
can be used for customized data generation to support AI training
From observing to predicting single-cell structure and function with high-throughput/high-content microscopy
Abstract In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular—powered by breakthroughs in computer vision, large-scale image analysis and machine learning—high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell ‘omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function