Search Results for author:
Found 10 papers, 6 papers with code
Deep-learning-powered data analysis in plankton ecology
The implementation of deep learning algorithms has brought new perspectives to plankton ecology.
Preface: Characterisation of Physical Processes from Anomalous Diffusion Data
Preface to the special issue "Characterisation of Physical Processes from Anomalous Diffusion Data" associated with the Anomalous Diffusion Challenge ( https://andi-challenge. org ) and published in Journal of Physics A: Mathematical and Theoretical.
Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps
Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs' dystrophy is challenging due to the presence of dark image regions called guttae.
Single-shot self-supervised particle tracking
Here, we propose a novel deep-learning method, named LodeSTAR (Low-shot deep Symmetric Tracking And Regression), that learns to tracks objects with sub-pixel accuracy from a single unlabeled experimental image.
Microplankton life histories revealed by holographic microscopy and deep learning
The marine microbial food web plays a central role in the global carbon cycle.
Neural Network Training with Highly Incomplete Datasets
Since the data to train the neural networks need to be complete, most studies discard the incomplete datapoints, which reduces the size of the training data, or impute the missing features, which can lead to artefacts.
Improving epidemic testing and containment strategies using machine learning
Containment of epidemic outbreaks entails great societal and economic costs.
Quantitative Digital Microscopy with Deep Learning
We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification.
Image and Video Processing Soft Condensed Matter Optics
Digital video microscopy enhanced by deep learning
We show that DeepTrack outperforms algorithmic approaches, especially in the presence of noise and under poor illumination conditions.
Soft Condensed Matter Optics
Intracavity Optical Trapping
Standard optical tweezers rely on optical forces that arise when a focused laser beam interacts with a microscopic particle: scattering forces, which push the particle along the beam direction, and gradient forces, which attract it towards the high-intensity focal spot.
Optics
