At the beginning of the project, the project participants acquire general and medical-specific Deep Learning methodologies and learn how to handle the software and hardware resources used by the Fraunhofer Institute for Digital Medicine MEVIS. Subsequently, the project participants focus on identifying and implementing specific sub-projects, thus making a contribution to a highly topical research area.
Main focus 2022/2023
- Data annotation via extended SATORI Tool
- Transfer Learning
- Training of different classifiers for the detection of prostate removal in specimen bag
- Inference dashboard extension for SATORI
Deep Learning in Medical Imaging
© Haslob Kruse + Partner
The project participants have the following, special software resources available through the Fraunhofer Institute for Digital Medicine MEVIS:
- MeVisLab - a framework for medical image processing and visualization in research and development.
- RedLeaf - a Deep Learning Framework developed by the Fraunhofer Institute for Digital Medicine MEVIS. RedLeaf builds on widely used Deep Learning Frameworks such as Keras and PyTorch. In addition to the integration of MeVisLab, RedLeaf offers numerous predefined neural network architectures and live monitoring of training performance.
- Challengr - a web interface developed by the Fraunhofer Institute for Digital Medicine MEVIS for comparing different neural networks.
Prior Knowledge and Requirements
Initial knowledge in Deep Learning facilitates project entry, but is not absolutely necessary due to the intensive theoretical training. Prior knowledge in the following areas is also helpful:
Python is a programming language with clear syntax and easy readability, which can be used versatilely. In the project, the Deep Learning Frameworks Tensorflow/Keras and PyTorch as well as the WebFramework Flask are also used.
Docker is freely available software that simplifies the deployment of applications: the applications are packaged into containers that contain all the packages needed for execution (libraries, system tools, environment variables, etc.).
Quasar is a freely available, Vue.js-based framework for frontend development.