A Hitchhiker's guide through the bio‐image analysis software universe

TL;DR: This study presents a double AMIS-ensemble deep learning model for skin cancer classification, achieving 99.4% accuracy and surpassing state-of-the-art models, with high usability and data security, aiding dermatologists in accurate diagnosis and integrating into workflows.

TL;DR: Important computational methods aiding live imaging and carrying out key tasks such as drift correction, denoising, super-resolution imaging, artificial labeling, tracking, and time series analysis are briefly covered.

TL;DR: In this article , the authors introduce common collaborator requests and corresponding potential services core-facilities can offer and discuss potential competing interests between the targeted missions and implementations of services to guide decision makers and corefacility founders to circumvent common pitfalls.

TL;DR: High throughput nanoparticle design using machine learning approaches is challenging due to the large combinatorial space and complex structure-function relationships.

TL;DR: Using a deep learning approach to track user-defined body parts during various behaviors across multiple species, the authors show that their toolbox, called DeepLabCut, can achieve human accuracy with only a few hundred frames of training data.

TL;DR: It is shown that on the basis of open-source software development, a fully functional software package can be created that covers the needs of a large part of the scanning probe microscopy user community.

TL;DR: QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images, making it suitable for a wide range of additional image analysis applications across biomedical research.

TL;DR: An introduction to the R project for statistical computing (www.R-project.org) is presented to make the professional community aware of "R" as a potent and free software for graphical and statistical analysis of medical data.