Massively parallel Monte Carlo diffusion MR simulator written in Python.

Last update: Nov 11, 2022

Overview

Disimpy

Disimpy is a Python package for generating simulated diffusion-weighted MR signals that can be useful in the development and validation of data acquisition and analysis methods. The data is generated by Monte Carlo random walk simulations that run in massively parallel on Nvidia CUDA-capable GPUs. If you use Disimpy in work that leads to a scientific publication, please cite [1], where the details about signal generation can also be found.

Requirements and installation

Follow the installation instructions.

Usage example

Read the tutorial to learn how to use Disimpy.

Validation

Disimpy's functionality has been validated by comparing its results to analytical solutions and to results from other simulators (e.g., Camino and MISST), and by automated testing (disimpy.tests). Examples of simulations used for validation are provided here. However, Disimpy is research software and some bugs undoubtedly remain. If you find any of them or encounter unexpected behaviour, please open an issue on GitHub.

Contribute

If you want to contribute to the development of Disimpy, start by reading the contributing guidelines.

Support

If you have questions or need help, open an issue on Github.

References

[1]	Kerkelä et al., (2020). Disimpy: A massively parallel Monte Carlo simulator for generating diffusion-weighted MRI data in Python. Journal of Open Source Software, 5(52), 2527. https://doi.org/10.21105/joss.02527

Massively parallel Monte Carlo diffusion MR simulator written in Python.

Related tags

Overview

Disimpy

Requirements and installation

Usage example

Validation

Contribute

Support

References

Owner

Leevi

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Massively parallel Monte Carlo diffusion MR simulator written in Python.

Related tags

Overview

Disimpy

Requirements and installation

Usage example

Validation

Contribute

Support

References

Owner

Leevi

No-reference Image Quality Assessment(NIQA) Algorithms (BRISQUE, NIQE, PIQE, RankIQA, MetaIQA)

Boosted CVaR Classification (NeurIPS 2021)

Code for BMVC2021 paper "Boundary Guided Context Aggregation for Semantic Segmentation"

Decision Transformer: A brand new Offline RL Pattern

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.

Erpnext app for make employee salary on payroll entry based on one or more project with percentage for all project equal 100 %

Official implementation of our paper "Learning to Bootstrap for Combating Label Noise"

STEAL - Learning Semantic Boundaries from Noisy Annotations (CVPR 2019)

NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.

Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE

Semantic similarity computation with different state-of-the-art metrics

Source code for the paper "Periodic Traveling Waves in an Integro-Difference Equation With Non-Monotonic Growth and Strong Allee Effect"

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The tl;dr on a few notable transformer/language model papers + other papers (alignment, memorization, etc).

Atomistic Line Graph Neural Network

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Code for the CVPR 2021 paper "Triple-cooperative Video Shadow Detection"

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