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Welcome to the MIScnn wiki!
This wiki is currently under construction!
So, dead links to non existent pages (yet) are possible :o
The open-source Python library MIScnn is an intuitive API allowing fast setup of medical image segmentation pipelines with state-of-the-art convolutional neural network and deep learning models in just a few lines of code.
MIScnn provides several core features:
- 2D/3D medical image segmentation for binary and multi-class problems
- Data I/O, preprocessing and data augmentation for biomedical images
- Patch-wise and full image analysis
- State-of-the-art deep learning model and metric library
- Intuitive and fast model utilization (training, prediction)
- Multiple automatic evaluation techniques (e.g. cross-validation)
- Custom model, data I/O, pre-/postprocessing and metric support
- Based on Keras with Tensorflow as backend
User friendliness:
Keras is an API designed for human beings, not machines. It puts user experience front and center. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.
Modularity:
A model is understood as a sequence or a graph of standalone, fully configurable modules that can be plugged together with as few restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are all standalone modules that you can combine to create new models.
Easy extensibility:
New modules are simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
Work with Python:
No separate models configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility.
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Medical Image Segmentation with Convolutional Neural Networks and deep learning.