Tivadar Danka

TL;DR: The key to the approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments.

TL;DR: The strengths and weaknesses of non-commercial phenotypic image analysis software are examined, recent developments in the field are covered, challenges are identified, and a perspective on future possibilities are given.

TL;DR: modAL as mentioned in this paper is a modular active learning framework for Python, aimed to make active learning research and practice simpler and make fast prototyping and easy extensibility possible, aiding the development of real-life active learning pipelines and novel algorithms.

TL;DR: This work presents a deep learning approach aiming towards a truly general method for localizing nuclei across a diverse range of assays and light microscopy modalities and outperforms the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions.

TL;DR: A deep learning-based algorithm provides an adaptable tool for determining hypocotyl or coleoptile length of different plant species, and it is shown that the accuracy of the method reaches human performance.