MIC-DKFZ
- Heidelberg, Germany
- https://www.dkfz.de/en/mic/
- mic-office@dkfz.de
Pinned repositories
Repositories
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TractSeg
Automatic White Matter Bundle Segmentation
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deep-glioma-growth
Code for deep learning-based glioma/tumor growth models
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batchgenerators
A framework for data augmentation for 2D and 3D image classification and segmentation
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HD-BET
MRI brain extraction tool
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gpconvcnp
Code for "GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data"
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medicaldetectiontoolkit
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
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RegRCNN
This repository holds the code framework used in the paper Reg R-CNN: Lesion Detection and Grading under Noisy Labels. It is a fork of MIC-DKFZ/medicaldetectiontoolkit with regression capabilites.
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Hyppopy
Hyppopy is a python toolbox for blackbox optimization. It's purpose is to offer a unified and easy to use interface to a collection of solver libraries.
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basic_unet_example
An example project of how to use a U-Net for segmentation on medical images with PyTorch.
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RTTB
Swiss army knife for radiotherapy analysis
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MatchPoint
MatchPoint is a translational image registration framework written in C++. It offers a standardized interface to utilize several registration algorithm resources (like ITK, plastimatch, elastix) easily in a host application.
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MITK-Diffusion
MITK Diffusion - Official part of the Medical Imaging Interaction Toolkit
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trixi
Manage your machine learning experiments with trixi - modular, reproducible, high fashion. An experiment infrastructure optimized for PyTorch, but flexible enough to work for your framework and your tastes.
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mood
Repository for the Medical Out-of-Distribution Analysis Challenge at MICCAI 2020.
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niicat
This is a tool to quickly preview nifti images on the terminal
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cmdint
CmdInterface enables detailed logging of command line and python experiments in a very lightweight manner (coding wise). It wraps your command line or python function calls in a few lines of python code and logs everything you might need to reproduce the experiment later on or to simply check what you did a couple of years ago.
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LIDC-IDRI-processing
Scripts for the preprocessing of LIDC-IDRI data
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PROUNET
Prostate U-net
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probabilistic_unet
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
