Data Augmentation is quintessential for training modern AI-based image analysis methods. Through careful modification of the available training data, the training distribution is extended and/or covered in more detail, reducing the potential for overfitting. Batchgenerators …

challengeR is a tool for analyzing and visualizing challenge results in the field of image analysis and beyond.

Challenges have become the de facto standard for benchmarking image analysis algorithms. While the number of challenges …

This solution contains a framework for training deep learning-based classification and regression models with Pytorch Lightning. It comprises several architectures, regularization, augmentation and training techniques and aims to provide an easy-to-use suite for experimenting and …

This repository contains an easy-to-use and flexibly customizable framework for developing and training semantic segmentation models. The focus was put on being usable out-of-the-box, without being a black box and giving the possibility to be …

When confronted with a new dataset, the application of AI-based segmentation methods is non-trivial. Substantial manual effort is required to optimize the network topology, preprocessing, training procedure and inference strategy to the task at hand. …