Deep Learning in Medical Imaging

In many of our projects, large collections of data emerge. Those big data sets stem from population-based studies, interdisciplinary clinical research projects, or from freely available crowd challenges. Using state-of-the-art machine learning techniques like Convolutional Neural Networks and other Deep Learning architectures, we explore the wealth of information contained in this clinical data.

 

Preparing your data – Training your AI – Building your solution

We support quantitative medicine with our modular software platform, QuantMed. Our goal is to enable more reliable, accurate, and efficient clinical decisions. QuantMed offers support along the way: Creating the reference training data, training deep learning models, validating them, and deploying the results into your quantitative diagnostic software. Improving a model only requires experts to correct its predictions, and to update the training data collection. With QuantMed, partners in different institutions can extract medical knowledge together, but without pooling the sensitive patient data. QuantMed nodes in each institution locally extract knowledge into knowledge modules. The QuantMed hub collects the knowledge modules from the nodes, fuses them, and re-distributes the update to the nodes. Knowledge modules are safe to share under data protection regulations. They do not contain any patient or institution-specific data, but all required technical information to use them in AI products.

We offer end-to-end support for translational research. Your idea, your data, your envisioned solution – our modular toolchain connects the dots.

 

Key features

QuantMed offers extended annotation capabilities for training your data
Systematic generation, accumulation, validation, and utilization of quantifiable medical knowledge
Full integration with MeVisLab for building your software solution

Preparing your data

Fraunhofer MEVIS offers software and services for annotating your data based on SATORI (Segmentation and Annotation Tool for Radiomics and Deep LearnIng).

 

Key features:

Configuration: easy configuration to your clinical problem and data.
Extensions: special annotation, visualization, and analysis tools can easily be integrated depending on your needs.
Integration: direct connection to deep learning framework so that annotations can directly be used.

 

Try out our free annotation tool MEVIS draw.

 

Training your AI

We support efficient model building based on the deep learning framework of your choice. All MEVIS tools are at your disposal, offering data preprocessing, cluster-based training, result validation, and deploying into any product. All you need to orchestrate your AI developments is a web browser.

The core of our training infrastructure is RedLeaf that allows for using arbitrary deep learning frameworks and connecting them with MeVisLab. Trained models can be directly used in MeVisLab and can also be exported for use in other applications.

 

 

 

Key features:

Web-based configuration and monitoring of trainings
Data prepcoessing and augmentation using MeVisLab
Distributed deep learning
Cluster support
Deployment via MeVisLab or TensorFlowServing

Building your solutions

Fraunhofer MEVIS develops solutions for directly integrating them into your products, including:

Segmentation
Registration
Detection
Classification
Bodypart Regression
Endoscopic Video Analysis
Image Reconstruction
Radiomics analysis

© Fraunhofer MEVIS
© Fraunhofer MEVIS