FastSurfer is a fast and accurate deep-learning pipeline for the analysis of human brain MRI. FastSurfer provides a fully compatible FreeSurfer alternative for volumetric and surface-based thickness analysis, also supporting sub-mm resolutions, and sub-segmentation of neuroanatomical structures.
LiberTEM is an open source platform for high-throughput distributed processing of large-scale binary data sets and live data streams using a stream-based programming model. The current focus is pixelated scanning transmission electron microscopy (4DSTEM) and scanning electron beam diffraction data.
SeisBench is an open-source Python toolbox for machine learning in seismology. It brings together the whole machine learning model lifecycle: datasets and benchmarks, models and training pipelines, and efficient implementations for deploying the models in production.
Heat is a flexible and seamless open-source software for high performance data analytics and machine learning. It provides highly optimized algorithms and data structures for tensor computations using CPUs, GPUs and distributed cluster systems on top of MPI.
DL4PuDe is an AI-based framework that automatically detects pushing behavior in crowd videos, leading to a better understanding of pushing dynamics. This knowledge is crucial for better crowd management strategies and better design of public places, making public places more comfortable and safer.
The ELIAS 2.0 software allows the identification of the footprints of rapidly ascending air streams in the atmosphere at substantially reduced computational costs compared to a conventional trajectory approach.
Kaapana is an open source toolkit for state of the art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging.
JuPedSim is a framework designed to support students and researchers in investigating pedestrian dynamics and conducting research related to the development and validation of new models or model features. It enables the analysis of experiments and facilitates the proper visualization of results.
A cross-platform, open-source inverse modeling toolbox for conventional and Fourier ptychography
BioAutoML automatically runs an end-to-end ML pipeline that can be effectively employed by non-experts. BioAutoML does not require specialized human assistance, i.e., it only needs a training dataset of biological sequences (FASTA files) to perform an end-to-end ML experiment.
pySDC is a Python implementation of the spectral deferred correction approach and its flavors, esp. the parallel-in-time extension PFASST. It is intended for rapid prototyping and educational purposes. New ideas can be tested and first toy problems can be easily implemented.
MLAir (Machine Learning on Air data) is an environment that simplifies and accelerates the creation of new machine learning (ML) models for the analysis and forecasting of meteorological and air quality time series.