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.
A framework for data augmentation for 2D and 3D image classification and segmentation
nnDetection is a self-configuring framework for 3D (volumetric) medical object detection which can be applied to new data sets without manual intervention. It includes guides for 12 data sets that were used to develop and evaluate the performance of the proposed method.
An example project of how to use a U-Net for segmentation on medical images with PyTorch.
MITK Diffusion - Official part of the Medical Imaging Interaction Toolkit
:dart: Deep Learning Framework for Image Classification & Regression in Pytorch for Fast Experiments
Code for deep learning-based glioma/tumor growth models
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.
An extension of YOLOv5 to non-natural images together with 5-Fold Cross-Validation
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.